Q: Tell us a little bit about yourself and your role at Scale.
A: My name is Sasha, and I'm a Machine Learning Engineer here at Scale. I've been at Scale for about a year and eight months. Previously, I was doing a master's program in computer science at Stanford, where my concentration was in artificial intelligence.
Within my career at Scale, I've worn a lot of different hats. I first started working on the Nucleus team, in a backend software engineering role when the team was very young. It was Scale’s newest product at that point, and I helped build out a lot of Python SDK interfaces, and the backend schema for a lot of new features, including Autotag, which is the edge case detection feature.
I later transitioned to building out other new products, including Scale Validate, which is the model scenario testing and validation product area.
Most recently, I've recently transitioned to our Machine Learning team full time, where I'm on the Content Understanding team. I'm basically helping to support automation in labeling projects associated with content categorization and curation type labeling projects.
Q: Could you give us a little bit of detail about your educational background, and how that led you to join Scale?
A: My undergrad degree was in applied mathematics. Entering college, I didn't think of myself as a coding person at all. But during the years I was at Stanford, computer science was something that a lot of the undergraduate body talked about, and the entry level computer science classes were really fun.
During my junior and senior years in college, I started taking some entry level machine learning classes. The first thing I ever learned about was logistic regression, and how you assign importance or attribution to different input features to answer social science questions. Like, is it age, or education, that contributes more to this downstream factor?
I thought that was really interesting, and really wanted to double down on that. So, that's what led me to stay for a fifth year master's, and specialize in AI. I worked as a research assistant briefly, in a sociology lab, where the objective of the sociology lab was to develop a dataset, documenting urban change. My job as a research assistant was to label data on AWS, Mechanical Turk. We had a lot of difficulty controlling quality, and getting annotations that were detailed enough to model the downstream tasks of trash detection and vandalism detection.
So, I knew that data collection was a hair on fire problem for a lot of application-driven teams. When I interviewed at Scale, I felt like the mission, and the problems that the company was trying to solve, were very urgent and impactful.
Q: What attracted you to working at a hyper growth startup?
A: When I was in college, I think a lot of my peers were talking about going to work at a big tech company; that was the prestigious choice. It was safe; it was a known outcome. At first, I was really focused on that, and I did a lot of internships at big companies.
And then I got this career advice my sophomore junior year of college, from a woman at Grace Hopper, and her advice was get on a rocket ship. Basically, the idea was, if you could just put yourself in a place where there were interesting problems to solve, some of those were going to come your way.
I really took that to heart, and my impression of myself started to shift toward somebody who really wanted to tackle big challenges and wear a lot of hats in their job. So, I joined Scale under the hypothesis that I was going to have an opportunity to wear a lot more hats and serve a lot of different functions at a very lean company.
As a result of having the opportunity to take on difficult challenges, I've grown my skills at a very quick rate. And I think I gained seniority and experience, at a rate that far exceeds any previous work opportunity I had in the past.
Q: What's your advice to students, or other people, who are interested in careers in machine learning?
A: First of all, I think it's an excellent career to be in, in that there are a lot of opportunities in the space right now. And the number of positions for machine learning engineers and machine learning-adjacent engineers, is growing really rapidly. I think there are also a lot of exciting developments, both on the research and applications side.
One thing that fascinates me about machine learning is how closely it can relate to real-world problems. If you’re interested in sociology, there's probably a place for that type of modeling for you. Or, if you're interested in online advertising, there are a lot of different application areas to concentrate on.
The other thing that I would recommend is really reading a lot. There are a lot of great resources online for practicing building deep learning models, whether it be through Kaggle challenges, or what have you. Also, a lot of research papers and academic work are publicly available.
One thing that I felt intimidated about when I started in the field was, how much there was to know, and how little of it I did know, as somebody that was early in their career. And I think making a habit of reading papers, and reading about approaches or problems that I didn't work on directly on a day-to-day basis, really increased my confidence.
Q: Has working at Scale shifted your perspective on AI or ML applications at all?
A: Definitely. When I first started at Scale, what I was really interested in was machine learning modeling work, and the idea of experimentation–building different neural network architectures, and being very scientific about, whether A works better than B.
One thing that's become really apparent, both working at Scale, and seeing the types of problems that our engineering teams face, but also working with Scale customers, is the importance of systems design within large scale machine learning systems. I’ve seen how difficult the problems of scalability, observability and overall reliability of machine learning systems are.
I really think that that's where a lot of innovation in the next 10 years is going to come from; not necessarily the modeling techniques themselves, but more, how you might integrate those modeling techniques into a larger scale operational framework.
Q: What do you like to do outside of work?
A: I've recently gotten into cycling. And so, I spend a lot of my time outside, whether it's running or riding my bike in the Bay Area. I also love wheel pottery. So on the weekends, you'll find me at the pottery studio.
