How to become a machine learning engineer at Twitter

Jigyasa Grover, a machine learning engineer at Twitter, tells us about her successful career path through internships and research projects.

We also talk about:
  • what a machine learning engineer does,
  • how to get started as a machine learning engineer,
  • open source and Google’s summer of code projects,
  • and her role at Facebook combing data and software engineering.
Picture of Jigyasa Grover
About Jigyasa Grover
Jigyasa Grover, is a machine learning engineer at Twitter.
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Read the whole episode "How to become a machine learning engineer at Twitter" (Transcript)

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Michaela:[00:00:00] Hello, and welcome to the Software Engineering Unlocked podcast. I'm your host, Dr. Michaela. And today I have the pleasure to talk to Jigyasa Grover. But before we start, I just want to tell you that I'm taking a short summer break after this episode. This means that the next episode will be live by the end of August. There are many reasons for this break. First, I really want to focus on writing the code review book right now. Also, I want to reflect on the past episodes of the podcast and maybe spice things up a little bit this coming fall. Finally, life is too short to get caught up in a hamster wheel, so taking a break and enjoying life is super important. And after that I'm back at full energy, bringing more wonderful episodes, and also with a bunch of new dates for my code review workshops. But let's get started with learning more about Jigyasa Grover, who is a Machine Learning engineer at Twitter. She's also involved in many communities that strive to close the gender gap and bring more people into open source. In 2017, she won the Red Hat Academic Award for her open source contributions. So I'm super thrilled to have her on my show. Thank you for being here, Jigyasa.

Jigyasa: [00:01:05] Thank you, Michaela. I'm so excited to be here with you today.

Michaela:[00:01:10] Yeah, I'm also really excited. So you're my first Machine Learning engineer that I have on my show. Normally I talk to software engineers, or maybe product managers or CEOs. So, can you tell me a little bit? What does a Machine Learning engineer do, and how does your day to day work life look like?

Jigyasa: [00:01:30] Sure. First of all, I'm honored to be your first ML engineer guest on your show. And if I talk about a high level explanation of, like, expectations from what an ML engineer does. So in a corporate setting, the task of an ML engineer would be to bring the element of artificial intelligence to business logic. It would be their job to build models, either from scratch, or use off the shelf technologies, using exciting, like, already existing frameworks with an added layer of their own variant and tooling. They also help sculpt the format of the data that goes into the Machine Learning training or prediction services, and not only they build state of the art models, but they also help them productionize at scale, which is one of the key things they have to do if they work at big companies or startups that cater to millions of people at once. If I talk about how a day in my life looks like, I would like to say from my experience so far that each day is very, very different for every Machine Learning engineer. Some days it would be like endless exploration of data, trying to find patterns and curate some useful features, which I would also term as feature engineering. Some days we would try as a team to build a model from scratch for a specific problem or tune our old existing models to, you know, catch up to up-to-date data. If they are working on, like, an online mechanism. Some days it would be like setting up experiments, trying out different combinations of features using different discretizers, performing grid, search and list hyperparameters, experimenting with techniques to fasten the training process, keeping up with the latest research by, you know, reading research papers, analyzing results from experiments, tying them to business prep, product metrics, and so on. So basically the tasks are endless, and a day in my life would look so much different every day.

Michaela:[00:03:28] Yeah, that sounds very exciting. So you are now a Machine Learning engineer at Twitter. How long have you been at Twitter? And did you start as a Machine Learning engineer? Like did you interview for that position or did you somehow grow into that position?

Jigyasa: [00:03:42] No. I specifically interviewed for the Machine Learning engineer role at Twitter, and it's been more than a year and a half since I've been working at Twitter. I work in the ads prediction team, and I'm fortunate enough that I was able to not only interview for the role and I also bagged it. So yeah.

Michaela:[00:04:02] Okay. That sounds really exciting. So, but have you been a Machine Learning engineer before you were interviewed for that? Have you worked at another company? And did you do that already before?

Jigyasa: [00:04:13] Yeah, so the thing is, before I came to know about Machine Learning, I've done like a lot of software engineering and open source contributions. I've also worked at, you know, research institutions and for a brief period, I also worked at Facebook, which had a very interesting intersection of software engineering, data science, and ads business. It gave me a great insight into how companies leverage data and feed their systems, thus making billions of revenue.

Michaela:[00:04:42] Yeah, that sounds really interesting. So when I was working at Microsoft, I worked, I had more or less a researcher role at the beginning. And so I wasn't a Machine Learning engineer, but we had to do a lot with data. So we were analyzing data, we were collecting data, different kinds of data, right. So I looked mostly at engineering data, right? So internal engineering data, for example, commit logs and, you know, communication information and things like that. And so one of the things that, I grew somehow into that position, right. From, from a research background then to software engineering background. And then because I had to work with this data, obviously I worked or I learned how to, you know, analyze this data. And this was also part of my PhD as well, but. I think then, then Machine Learning is another, you know, it's another facet of that whole thing. Right? So I applied sometimes Machine Learning algorithm, but everything was out of the box. I never created my own Machine Learning algorithm or something, but I, I worked a little bit with that. So, how did you make this transition from, you know, the software engineering to Machine Learning? Did you do a course or was it really hands on at the company that you learned at, how, how did that transition work?

Jigyasa: [00:06:01] Sure. So I kickstarted my journey by contributing to a lot of open source projects while doing my Bachelor's degree in Computer Science. So why I took bachelor's degree, like, why did I decided to do bachelor's in Computer Science was something that my parents asked me, like my dad himself is a computer professional and he works for the Government of India as senior technical director. And then he asked me to, you know, pursue something in Computer Science because he saw that in a spark in me maybe. And then that's where I started, but I mostly was, you know, contributing to a lot of open source projects and, you know, working with Pharo, JavaScript, you know, web technologies, Android technologies, and so on. And with that, I also started participating in a summer program, Google Summer of Code, which basically takes thousands of students all across the world, and you know, makes them contribute to a lot of open source projects. And one summer, that was my second time in the summer program, I worked on a project called Suzy, which was aiming to build an open source digital voice assistant, just like Siri, Cortana, et cetera, but open source. So that's where something about, you know, natural language processing and Machine Learning sparked an interest in me. So I started taking up some ML courses in university and I wanted to do something further, and that's when I applied for research internships and, you know, National Research Council of Canada, Institute of Research and Development, France, and got sponsorship from a prestigious, international organization, which I'm very grateful for. And that's where my interest in Machine Learning and Data Science intensified. I ended up, you know, working with postdocs and professors at these research institutions, talked to professors, worked on a lot of research papers, and that's when I decided that I should pursue an advanced degree. And I graduated from the University of California, San Diego with my master's degree. You know, with specialization and focus area of Machine Learning and Artificial Intelligence. And that's how my journey, you know, basically graduated from a normal computer science and a software engineering background, with a lot of open source contribution still, ultimately being a Machine Learning engineer, and that's when I said I worked at Facebook for a brief period, working on intersection of data science and ads business. And I also gained an interest in ads business and then I eventually moved to Twitter too, as a pure Machine Learning engineer, and it's been a wonderful experience so far. And I'm obviously so much grateful for all the journey that I've had.

Michaela:[00:08:32] Yeah, it sounds really amazing. I mean, I heard also a lot of different countries, a lot of different cities. Uh, which reminds me of my time when I traveled a lot. But, so you were traveling as well. You were traveling a lot. Were you traveling alone or did you bring your family or how was that period for you?

Jigyasa: [00:08:51] So during that time, when I was contributing a lot to open source projects and these sorts of internships, I was also helping help bridge the gender gap. So I used to work with a lot of, you know, women who code organizations, women technicals and so on. And because of all these, I am so honored to have been felicitated by Red Hat as a Women in Open Source Academic Award back in the year 2017. And that award, you know, the global award and recognition gave me actually wings. And I started getting invitations from so many different conferences and summer schools and so on, like all throughout the world. And I would, like, get to travel. And during all these times, like even to the date, I've traveled to more than 15 countries all alone. And mostly I go for work. And even within the United States, I've traveled to like most of the states, I've covered 50% of the States of the United States, just for line of work, going to conferences, traveling, speaking at them, holding workshops, et cetera. And to be honest, traveling has opened my eyes in so many other ways, I get to experience different cultures. I get to meet people and I also get to see how is the working temperament in different countries. And yeah. So, I would say traveling definitely helps you, but yeah, these days, the times are such that everything's virtual. All of my travels are virtual.

Michaela:[00:10:15] Yeah, that's true. Yeah, I'm actually really, I'm really happy that I did my last travel in February, just before the lock-down, yeah, I came back and I was like, I'm really happy I did that! Because now we are all stuck, right? I canceled all my travel plans. I should have gone to so many nice countries. Yeah. I'm really missing the travel, but I also, so you're, you're traveling and then for your internships, you actually stay a longer time in different areas, right? So, because internships normally are like three months, or what is the time period that you did your internships?

Jigyasa: [00:10:49] Yeah, same. So in Canada I did for three months and same goes for France in Paris. Michaela:[00:10:54] Okay. Okay. And then you also moved, right? So, because I, for example, I traveled a lot, but I also moved to different countries. So I lived, which means, I would say living is more than six months, so that you really have to open a bank account, you know, find a proper, a proper place to stay, where you cannot live out of the hotel because it gets too expensive. So I lived in the UK, in Canada, in the US, in Germany, in Austria, and in the Netherlands.

Jigyasa: [00:11:21] Oh, wow.

Michaela:[00:11:21] How about you? I heard also that there were like, uh, a few countries, right, and cities?

Jigyasa: [00:11:28] Yeah so, I mostly grew up in India and I've lived with my parents. And then as I said, I moved to San Diego to pursue my master's degree, and I've lived there for around, I would say two years. And then. Or maybe less like in a year and a half. Yeah. And then I eventually moved to San Francisco. So, living alone is of course a different experience altogether, but I also enjoy the freedom that I get and yeah. Embracing different cultures is what I like.

Michaela:[00:11:57] Yeah. Yeah. I can imagine it. I mean, coming from India and then being in the United States must be quite eye opening. I mean, I moved from, I moved the first big move that I made was from Austria to London, and then to the Netherlands. But somehow, because it was all European countries, I thought, you know, it's all the same, which wasn't true. Right? Like, so I got a big shock when I realized that people, you know, it seems like we are, we're very similar in culture, right? Western culture and things like that. But then we are very different. Like the values, especially the values were something that were really difficult for me. Like when it comes to things like, going to the doctors and what experiences or expectations you can have there. Right. Or how they would, you know, what they would treat as a serious illness and not so serious illness, those things, right. I didn't expect that this will make a difference, but it did. But I can imagine that coming from India and then going to, you know, the United States, is even a bigger, a bigger difference. How was that for you?

Jigyasa: [00:12:56] Trust me, I'm still scared of the United States healthcare system that I haven't stepped out of my apartment since four months ago, because I'm so scared of getting sick. So yeah, the cultural side, of course, like really different, but I value both of them because you know, one is very close to my heart and the other is very close to how I work and all, but I'm trying to, you know, pick the best of both worlds, and yeah, as you said, things might look same on, you know, from a bird's point of view, but when you go deep inside, but in the case of Indian and United States, they are completely different and yeah, but the thing is when I go back home, I enjoy that because I've always grown up there, so.

Michaela:[00:13:36] Yeah. Yeah. And so, in India, where did you grow up? Was that like in a big city or in the countryside?

Jigyasa: [00:13:44] So in India, I grew up in a place called Chandigarh, which is the capital of the states Punjab and Haryana, like both the states share the capital city. And then it's looking through in the foothills of the Himalayas. So it's a very pretty town, I would say, it's not super big, but yeah it's growing. And then I went to like a proper convent school, like a Christian convent school and it was like, so great, like all girls convent school, so life was so different. So yeah, so not something that I experienced, like the outside world was like completely different from what I experienced in that all girls convent school.

Michaela:[00:14:18] Yeah, I can imagine. So, one of the things I want to talk with you a little bit about is your experiences at Twitter, but also maybe at Facebook for software engineering practices and Machine Learning engineering, or I call it now data engineering, right. Or Data scientists. So when I was working at Microsoft, maybe I was in a very specific situation, right. So I was one of those research has slash software engineers, right? In this large software engineering team. And so all the data engineering and, you know, the research obligations were on my shoulders, but there weren't a lot of others, right. So we were always the driving forces, right, for those things. And then we informed like the, the other people that were doing the implementations or, you know, like we were, for example, making the prototypes and they were doing the real implementation down and things like that. Right. But what I realized that, is comparing with software engineering practices, the practices that we had as data engineers, they were less sophisticated. There was less rigor, right. There were still a lot of questions on how do we test our data efficiently, right? How do we review our code or, not only the code because the code is probably not that important, right? It's the data, and how do we version our data? And somehow those questions seem quite unanswered, a little bit, in this dark data science world versus what we have in engineering, software engineering world, right. You know, it's like a push of a button and you know, like you have your, your DevOps chains that are automatically doing everything for you. How is that for you? Do you have like this software engineering rigor as well, in this data engineering world. And do you feel also, like, I felt sometimes like, so lonely ranger more or less, right. Because I was surrounded by a lot of software engineers, but then the data science tasks, right, were only on my shoulder, maybe there was one or two other people that we occasionally talked about what's happening. But I guess at Twitter, in your team, it seems like you're a bigger team where there are a lot of people that you can exchange your ideas and things like that.

Jigyasa: [00:16:29] Yeah, Michaela. So in that I would totally want to echo, like software engineering practices are more like, sort of rigid, like if you know, you follow the agile method or the waterfall software development method, you always have like a strict set of rules, like after this, you will do testing, and if it doesn't work, you go back to the steps. So the rule book is like, one, two, three, and you follow the bullet points, like straight up to the fleet (??), but in Machine Learning engineering or data engineering, as you said, things are not at all rigid because they, they are mostly trying to solve open ended questions. There's no like strict solution to a question, right. You always have to do a lot of, it's more of like, I like to think of it as science, where you know, like a chemistry experiment. So you have to do a lot of experiments, until the thing works. So that's why you cannot put like a time, you cannot timebox any experiment, because you never know the model training would take two extra hours than what you thought of. Or it could take two extra days. And then sometimes after running the entire module, you would realize it doesn't work as I expected. So you have to like rerun all over again, whereas in software engineering, I think that the idea of open-ended-ness or the idea of ambiguity is less as compared to data engineering or Machine Learning engineering, hence it's easier to timebox those things. It can easily be said, oh, after one week from now, I want, you know, this UI to be present. So it's much more feasible than what you set expectations or milestones in Machine Learning engineering. Hence, even our team at Purdue does not follow these strict, because I guess everyone in there is from a Machine Learning background. Like most of the people have PhDs, or masters degree. So the very well known would be how experimentation goes. So we are mostly allowed to, you know, free form it and deliver as and when we think the model is performing as per our expectations.

Michaela:[00:18:23] Yeah, that's really good. I mean, my experience was when I started and we had like this team, then we had a very, very great leader, like a wonderful manager. That was also a reason why I actually joined the team at Microsoft. And he absolutely understood the exploratory nature of our work, right and the research side that we had. And that, you know, as you said, you cannot timebox it, like you cannot say, oh, we are going to solve that problem, and in six weeks, we are done, right. Because sometimes you don't, you don't even know if you are able to solve the problem, you know, in a good way, in a good enough way, like really makes sense. Right? Otherwise it wouldn't be research. And then at the very end of my, my time at Microsoft, we had like a reorg, and I got a completely different manager and he tried to, um, put us into scrum. You know, time-frames and things like that.

Jigyasa: [00:19:16] Yeah. Does not work, yeah.

Michaela:[00:19:16] Always had to predict, you know? And, he always had, but there must be a positive answer at the end, right, and it must work. And then I said, well, if I can only try out what will work in the end, right. Then this is not research, right. It's not a bold enough question if you know that it will work from, from the get go, right? Or if you know how long it will take. Yeah. So there was a big clash between different, you know, ways of working. So, yeah. Interesting. So you are a group of Machine Learning engineers that really understand that the nature of the thing that you're doing, was it the same as at Facebook? Or was that a different experience there?

Jigyasa: [00:20:00] So at Facebook, as I said, my role was not pure Machine Learning. It was like a mix of software engineering, data science and ads business logic and so on. So yeah, the working side was completely different. There they followed like strict set, oh, by this, this should be done. And, you know, doing like a strict bag. And of course that company is more advanced, like, more mature in terms of engineering. So, yeah, things were completely different, but I enjoyed working there. I enjoyed like both the sides of the coin, so. But it depends on the role, I guess. Yeah.

Michaela:[00:20:31] Yeah. And so how do you review, for example, your code, do you review it, or do you work together with other people that are looking at what you're producing, or maybe at the data that you're working on, and how do you test your systems? Or do you have like a methodology in place there for doing that?

Jigyasa: [00:20:50] Yeah, sure. So for testing, of course we have like different, you know, services that we use and different expectations that we want, the, you know, the product or whatever model that we build to satisfy. And as I said, we have a team of like all Machine Learning engineers and people who come from like very advanced backgrounds. Many of them are very experienced. So they obviously help me as I'm growing in my role. So review process, they help. And then for, as I said, for expectations, we have them set already, and then we rigorously keep testing the model on different sets of data to see how it's performing and is it, you know, as per our expectations and so on.

Michaela:[00:21:31] And so now you're in San Francisco and while there's COVID happening right now, the pandemic, do you, do you go to the office or are you all working from home now?

Jigyasa: [00:21:42] No, it's been more than four months that I've been working at home and Twitter has been completely supportive. They have provided, you know, all the budgets for setting up a work from home office, like a home office and so on. And til date they haven't asked us to come back to the office and I don't think that's going to happen anytime soon, but the good part is Jack, our CEO, he also announced, like. flexible working style. That is, you're free to be remote forever if you want to. So that's something that I'm really excited to look into, yeah.

Michaela:[00:22:17] Yeah, I can imagine this is opening up a lot of opportunities, I think. And so, do you see any drawbacks? Do you experience drawbacks working with your colleagues, some collaboration issues that you're having because of the remote set up?

Jigyasa: [00:22:33] I would like to say so far it's good, but of course I miss, you know, a little bit of the personal interactions we used to have, you know, it's like going for a coffee break and then you meet someone and then you're just chatting in the elevator, you meet someone or in the lunch line or something. Of course I miss those little personal interactions, but yeah, work-wise it hasn't been like a drastic change because we've been able to collaborate successfully virtually so far.

Michaela:[00:23:00] Yeah. So everybody is remote right now or are some people back in the office?

Jigyasa: [00:23:05] No, everyone's remote.

Michaela:[00:23:07] Yeah, I think that that helps, right? So if a few would be onsite and a few would be remote and it's probably harder, but now everybody is on the same, on the same, you know, has the same challenges. And so everybody has to work towards making it work, right?

Jigyasa: [00:23:23] Yeah, the conversations actually happened on the team channel now, it's not like, oh, someone, some of them are, you know, talking in person and the people who are remote, they feel like, oh, fear of missing out. Did I miss on some conversations? Like interesting ones. So yeah, everyone and everyone's remote. That definitely helps.

Michaela:[00:23:42] So I imagine, I mean, I feel like this, when I'm talking to you, you did so many things and it seems like you're super ambitious and you were very, very successful with your way. So can you tell us a little bit about how did you get into open source software and you know, how, what would you say, like for somebody at the beginning or, you know, that wants to advance their career? What are some really good steps that you thought well, these are some of those steps that really brought me forward because they are maybe a few steps that looking back, you think, well, this was really, like, leveraging or boosting my career. You already sound like this open source academic award really opened a lot of doors. Is there something else like that you said, well, if I would do it all over, this is exactly where I would focus my energy.

Jigyasa: [00:24:33] So I would like to share how I actually got started open source. It's actually a very interesting story. So I was pursuing my bachelor's degree, you know, as a normal college student would do, like back in those days would visit the library just to use like, you know, the high speed internet and I was calling on the phone mindlessly through all researches (??). So I was doing exactly the same and that's where I came across a tech conference that was happening, you know, on social media. And they mentioned something about Pharo. And then I heard the word Pharo, like, then I heard back in the day, like I only thought about, you know, monarchs of the ancient Egypt and I could not relate it to tech. So, you know, that day I just went back home and I did a quick Google search and that when I realized like Pharo with a P, H, A, R, O. It's an actually immersive programming experience and a powerful environment focus. Basically it's like an IDE and an OS rolled into one based on an object-oriented programming language known as Smalltalk. So I thought, hmm, really interesting to have like a very interesting name. And then I
started reading more about it. They had like internet, really a chat, like a chat link to join. I thought, you know, why not give it a try? Like. But I joined the chat, but I didn't know what to post. I saw lots of developers. Now back in those days, IRC was still really what open source developers used to talk to each other. So, you know, gathering all my courage. I talked to a few people in my university who were actually contributing to open source and said, oh, you know, they said, you can just go and say hi and I read their messages and on the blogs. So I gathered all my courage to enter that IRC link and just posted "Hey, my name is Jigyasa. I recently came to know about the word Pharo and this programming environment. I would love to know more about it." That's it, like that was my message. And it was my luck or something. There was a mentor from China named Martin who was online. And then he sent me like a couple of links to the documentations said, oh, you should read through these. I was like, okay, yeah, I'll try the examples, and the tutorials out, because the tutorial had, you know, links to basically, steps to install it, you know, make some very small UI games, tic-tac-toe and all that stuff. So I said, okay, sure. Why not? Summer break came. I was like enjoying at home. So I thought I'd get started, I opened the link and that days Pharo 3.0 was in the works and Pharo 4.0 was getting like, you know, traction. And they were like helping in productionizing it. So I tried an example basically, I had Pharo 4.0, and then I thought, oh, that's not work. And I changed that example, like changed some code and it started to work. So I went back to Martin and said, hey, I was trying this example. And then I see this works in Pharo 3, but not Pharo 4. And for Pharo 4, I had to do this change to make it working. And he said, oh, good find in the documentation. Why don't you go and open a pull request? I was like, what. And then he talked to me about, oh, I should open an issue first and then submit a pull request. I was like, okay. And unknowingly the small bug fix, turned out to be my first open source contribution. And that's when I got the hang of it. You know, I used to, not only trying examples, I ended up making my own games like, you know, Contact Manager and so on in Smalltalk. And then I got the opportunity to work on Google Summer of Code. For both times I've worked with the organization known as Fossasia and then I said, the second time is when my spark in Machine Learning and all interest intensified. So that's how, like I connect my journey from, you know, being a normal student at the college, you know, trying to get through all the Computer Science classes in the end, turning out to be now interested in Machine Learning and Data Sciences. So that's how my interest in open source started and it was, I owe it all to my curiosity, I would say. And the fun fact is, Jigyasa in Sanskrit actually means curiosity. So, yeah. So I guess I would always want to say like, however difficult things missing, but if you try, you know, these days you have so many, like just a quick Google search helps, so at least try to, you know, make sense of what you see, and yeah. Curiosity helps.

Michaela:[00:28:54] Yeah. And, and what you were saying is what I hear very often. People advise that if you want to get started in open source, the easiest foot into the door would be to start with the documentation, right. Make some changes and improvements to the documentation because it's, it's a little bit easier, but you also get to know the software and to get to know the people, because you also said, well, there was this mentor, right? So you have to get somebody that gives you, lends you a hand, right. On your, on your way into, into open source as well.

Jigyasa: [00:29:25] Yeah. So on that note, I would also want to say in my spirit to give back to the community, I want to be a mentor now. Like I've mentored people in so many global community programs like, Outreachy, Rails Girls Summer of Code, Google Coding, Google Summer of Code, and even like locally in all these communities, because I feel like there were so many mentors that helped me during my journey, nd it's my turn to give back. And regarding the views getting into open source, so people usually think like, coding is the only way to get into open source. Whereas I think that open source is a way of factuality, it's a way of collaboration and project ideas and thinking. So I also do like different talks and workshops at conferences where I talk about different ways to dive into open source, like diving deep into open source. It's not all about coding. So there are so many other ways you can, you know, get involved with open source, and the documentation is the one and the foremost.

Michaela:[00:30:22] Yeah. I think that, especially for me, for example, there is this probably social aspect that's also holding me back from contributing a little bit because, you know, you're, I would get intimidated quite quickly. Right. And then I feel like you, you need a little bit help to understand where you start or you, you know, very quickly people could say, oh, this is a dumb question. Or, you should have known, or, you know, be, be maybe a little bit rude to you. So I think that knowing how to approach different communities and having, you know, a mentor that helps you can be very, can be very helpful. Did you make any negative experiences in the open source community?

Jigyasa: [00:31:05] I would like to say, like so far, my experience has been mostly positive. Of course there have been like, like teeny tiny instances where I felt, oh, this is not the right way. For example, when I was a student and applying for Google Summer of Code. I would actually receive messages like from people, like received from like a couple of them asking me to not apply for a different project since they were also interested in applying for the same project and it would create a clash because then the mentor would have to choose like, which one of them like he should select. So, but I was very adamant because I was like, I was contributing to this. So maybe the mentors should just see like, who contributes the most or maybe who has the talent and it should be mostly based on, just because you eliminate competition does not mean there won't be any competition, and you'll go further, right. So you should have like confidence in yourself. And I think it comes with, it does not come overnight. I totally agree with the social aspect of, you know, being hesitant, I've been in your shoes. Initially when I used to contribute, like no one, no one other than maybe my father knew that I was contributing because I was so scared to, you know, share with people and like, oh, that I'm contributing to open source. So even my new GitHub handle was my first and like my initials initially, and then I changed it after a year because I realized that, the initials won't work, my initials, it was just like JG or something, because, and then I didn't even have a profile picture because I was very hesitant because I didn't want, you know, people, you know, as I said, judging, and I didn't even want people in my new class to know that I was doing something like that, but later on it changed. So it just took me a couple of months to get used to, and after that it was fine. I had my like full name up there, I had my profile picture. But it just took me like a couple of months, I would say.

Michaela:[00:32:48] Yeah it's probably, how I get into Twitch right now because I started streaming and it's also weird, right? Like you're streaming and you get to know what you actually have to do, or, you know, if you even like it or not, so I'm not announcing it a lot because I don't want, you know, a lot of people to actually watch. So I'm actually happy if there are only a few people on my stream because I want to get, you know, used to and grow into that new experience, I think. And I think when I, when I'm a little bit more confident, I will probably announce it a little bit more. Maybe I'm not even continuing who knows, right? But I think maybe I can relate to that for open source as well, right, so you make your tiny contributions and they are like a little bit hiding behind a pseudonym or you know, just your initials. And then if you're feeling confident, you actually can have that much more. So you were also talking about the bridging the gender gap. And so I know that you have been active in the Women Techmaker community, and also a Women Who Code community. How did you get into that? And are you still doing that? And now you told me that you're mentoring. Was that the same thing or was where there differing activities?

Jigyasa: [00:34:06] So I started off by, volunteering at local Women Who Code and Google Women TechMakers communities back home. And it initially started with, you know, sharing how I did my first open source contributions or, you know, helping people set up their GitHub profiles and, you know, fork projects and so on. And then later on I started, you know, holding workshops that I would talk about, you know, oh, let's build a game in Python, and so on. So I did that, you know, there, and then virtually, also in programs like, you know, Learn IT Girl, or Real School, Summer of Code, or Google Coding, I would just be a mentor. You know, if they had any questions or just questions and like not even technical, but how to approach the, you know, open source contributor or open source developer and so on. And I did that back home, and even here in San Francisco, I'm doing it. The physical aspect might have reduced because I have a full time job now, but I try to keep it virtually. For example, I would join, like, a workshop at a conference or just go to like, weekly coffee chats they have around here, the city (??) and so on. So that's how I've tried to, you know, give back to the community, because I've been helped a lot by mentors. I thought, like I should also help the other way round.

Michaela:[00:35:25] You know, what also would help, I think? If you would be open to share your interviewing experience, especially for me Machine Learning. How can I imagine that? How did you, you know, you applied for that, how does an interview look like for a Machine Learning engineer? What were your questions and how did you prepare? And yeah.

Jigyasa: [00:35:48] So yeah, for Machine Learning interview, I would say this is a little more trickier than a usual software engineering jobs because, you know, software engineering job interviewers would mostly focus on data structures and algorithms, whereas Machine Learning you are required, especially if you work in a big company, as I said, you not only build models on paper, you also have to help them productionize at scale. And for that, it requires engineering skills. So they test you on engineering skills. First, they would ask you to now implement data structures, algorithms, and all the usual shenanigans. And then later on, they would move on to, you know, specific Machine Learning questions. They would ask you what different kinds of models are they would give you a use case. And for that use case, you would have to think from top to bottom, like what kind of features, now you have to go creative, what kind of features you would want in a model? What kind of model would work the best? Supervised, unsupervised, SVM, PC analysis. All those. Feature importance, feature correlation, all these, what kind of metrics or what kind of loss functions would you base judge your model on? What is the objective function you would optimize on? And all of these things, you have to be like very thorough with the fundamentals. They will ask you the ins and outs of it. That's what my experience has been. I would suggest if you would have like, you know, fundamentals of Machine Learning clear, it would be like a very smooth interview, but make sure that you also have your engineering skills brushed up, like you would have know your data structures and algorithms as well.

Michaela:[00:37:25] And so did you have, like, did you have to code, did you whiteboard or how, how was that at Twitter? How long was the process also, like at Microsoft I think it was a whole day, probably eight to nine hours of interviewing.

Jigyasa: [00:37:38] Oh, that's long. That's long. Yeah.

Michaela:[00:37:39] Yeah. And it was really long. Yeah.

Jigyasa: [00:37:41] Yeah. I remember having three interviews of one hour each. And with like 20 minutes break between each or something. So it was around four to five hours, not longer than that. Yeah. And between that, they also had a round with my to be manager or to be tech lead where they would just ask like, you know, usual life questions or behavior questions, they would ask me like, you know what I expect from the job, just to know, like, if I'm the correct fit or not.

Michaela:[00:38:14] Okay. And so was it all on one day or did you have like a screening interview first and then you had this onsite interview?

Jigyasa: [00:38:22] Oh, for me, it was all in one day, I was actually approached by the manager of my current team via an email asking me to interview. So.

Michaela:[00:38:32] Okay. So you went straight to the onsite interview?

Jigyasa: [00:38:36] Yes. Yeah, fortunately. Yeah.

Michaela:[00:38:39] Okay. Yeah, that's really good. And so now that you're a Machine Learning engineer, what does the, is there some career path for Machine Learning engineers? Would you become a Machine Learning engineering manager, for example? Is that something that exists at Twitter or?

Jigyasa: [00:38:55] Yeah, definitely.

Michaela:[00:38:57] How does that work?

Jigyasa: [00:39:09] So we have like, as a Machine Learning engineer, of course we can grow on to be like a staff Machine Learning engineer, Principal Machine learning engineer and so on. But if you also want to get a little more inclined towards other managerial positions, you can, after you reach a certain level, you can also be like a Machine Learning engineer manager and Machine Learning, and so on. So what a Machine Learning engineer manager, like Machine Learning engineering manager would do, would be to manage a team of Machine Learning engineers. So that is something like, if that's the part you're looking for, like managing the team, you could do that. Or if you want to be like pure engineer, you can also pursue that path.

Michaela:[00:39:40] Okay. So what, what, what do you think that you will do? Do you know already ?

Jigyasa: [00:39:44] So far I'm, I don't plan like my life, like 10 years in advance or something like that. I try to take it, you know, step a day, but for now, if I see myself, I would, you know, continue the tech part as long as I can.

Michaela:[00:40:01] Yeah. That's, that's also what I really liked at Microsoft that you didn't have, you have to choose, like there was not like you're an engineer and then you're a manager as the next, you know, as a next step. But you had like this parallel, really technical track, and it wasn't called staff engineers, but it's like Principal. And then you would be like one of those technical leaders, right. So you could even become like, if you, if you're very ambitious, right, you would be this distinguished engineer, for example.

Jigyasa: [00:40:25] Yeah, same, yeah like a, yes.

Michaela:[00:40:27] you don't have to take over the management part, which I liked, right. Because very often in smaller companies, especially, there is like, if you want to advance your career, you have to switch from the technical side to the managing side, right?

Jigyasa: [00:40:40] Yeah. This is something that I like too, yes.

Michaela:[00:40:43] Yeah, that's really good to know. Okay, so, very cool. Thank you so much for being on my show today, for sharing everything that you learned on your way with us. I think it was very, very insightful for me and I hope also for my listeners. Is there something else that you would like to share with us and that we haven't talked about?

Jigyasa: [00:41:01] No, I guess I've touched most of the aspects. Thank you so much Michaela, it was like wonderful talking to you today.

Michaela:[00:41:07] Wonderful. Yeah. So I will link to your profile, to your GitHub profile, to your Twitter in the show notes. If you want to see for yourself, what Jigyasa is all about, just look at my show notes. And so, just have to say thank you, so much.

Jigyasa: [00:41:21] Thank you so much.

Michaela:[00:41:22] And have a good day. Okay. Bye bye.

Jigyasa: [00:41:25] You too, you too. Bye bye.

Michaela:[00:41:27] I hope you enjoyed another episode of the Software Engineering Unlocked podcast. Don't forget to subscribe, and I will talk to you after the summer break in a few weeks. Bye.

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