Dyno’s Data Science team hosted its first class of interns amidst the socially distant heat of summer 2020. Five outstanding individuals joined us to help tackle challenges in computational biology, machine learning, software engineering, and, of course, remote company building.  

These individuals left lasting contributions to our science and our culture. This post is an opportunity to hear their stories.

Our 2020 class of summer interns consisted of Stephen Malina (Graduate student at Columbia University), Tiwalayo Aina (rising senior at MIT), Stewart Slocum (rising senior at Johns Hopkins University), Jeff Chen (rising junior at MIT), and Maxwell Kazman (rising junior at Georgia Tech). Since interning at Dyno, Stephen decided to join us full time, Tiwa and Stewy have decided to go to graduate school, and Jeff and Max are continuing their undergraduate studies. 

What brought you to Dyno? How did it compare to what you expected?

Stewy: Going into my senior year and looking towards the future, I wanted to spend my summer with a smaller company in a machine learning role. Dyno was exciting to me for two reasons: because the problem space is interesting and impactful, and because I liked the company’s attitude. This summer, I worked mainly on sequence proposal strategies for the AAV cap gene, but also got to do some supervised model development and build ML infrastructure. Dyno’s work is on the frontiers of machine learning and biology, which gave me the chance to think deeply about how to do things that haven’t been done before. I had an absolute blast! I learned a ton and had a great time collaborating with others, especially my mentor and the other interns. The environment was very open, and I had a lot of fun working with such capable people. 

Jeff: I’ve been interested in the application of CS in the biotech space, much more seriously the last year or so and so when I learned about the novelty of Dyno’s data and experimental process, I was excited to have a chance to be a part of Dyno, especially in its high growth cycle. 

Max: Before I started my internship, I was expecting an environment similar to an academic research lab, as many AAViators came from this background. However, the culture at Dyno reminds me much more of a lean tech startup and everyone is excited to play a part in bringing our technology to the market. Much of the work is very academically rigorous and exploratory, but I also got to work on engineering and infrastructure problems, something I wasn’t expecting as much. Working at Dyno has been super exciting and has exposed me to a variety of interesting projects and challenges.

Tiwa: Coming from a background in quantitative finance, I had some experience building models, and I knew I wanted to apply that experience to another field. After meeting some data scientists from Dyno, I was introduced to Dyno’s mission—in particular, I learned about the novelty and difficulty of the problems Dyno aims to solve, and I was immediately interested. The experience was just as challenging as I anticipated and enhanced by the opportunity to meet with mentors and collaborate with other interns.


What did you enjoy most about being here at Dyno? 

Stephen: Besides the people, one thing I really like about Dyno is that we’re working on a problem that I feel is both important and part of a larger societal project in which I’m personally very invested. Regarding the former, I’m, I think rationally, optimistic about the prospect for engineering better AAVs to help prevent and cure genetic disease. When I let myself indulge my fantastical side, I imagine a future in which gene therapy is so safe & cheap that it’s ubiquitous and widely applied towards improving human health. Regarding the societal project I mentioned, I believe that understanding and learning to engineer biology is one of the most exciting long-term goals of the 21st century and view Dyno as one of the companies building a toolkit for doing this more efficiently and safely. 

Stewy: I really enjoyed being part of a company like Dyno that pushes boundaries and tries to define what is possible instead of just accepting it. Dyno’s mission is incredibly ambitious – it’s risky and seriously hard. But it feels great to be part of something you believe in, and I am confident that ML-guided design will be key to the future of gene therapy.

Jeff: I definitely enjoyed the science and the people the most. Giving a data scientist direction and rich data, is like setting him or her loose in a playground. It was very exciting for me to be able to make my own novel discoveries here and integrate my work into the Dyno pipeline. For the people, it was great to be surrounded by so many smart people that felt driven in what they do. That environment is refreshing. 

Tiwa: The thing I enjoy most here at Dyno is the academic rigor that many events have. I find it really cool that the whole company will listen to a person’s presentation on research they did. There are also Journal Clubs where we review academic literature in machine learning. We even had a lecture series where wet lab biologists would teach the data scientists about interesting topics in biology!


What was the most difficult aspect of working at Dyno?

Stewy: There was a lot of paper reading and wikipedia scouring over the first couple of weeks as I tried to orient myself in the context of ML-guided sequence design. Afterwards, the challenge was to do things that weren’t in papers. Dyno’s business is to try to solve unsolved problems, which is usually pretty difficult, but I always felt supported enough to get help when I needed it. I also appreciate my mentor’s reminders to not bite off more than I could chew, and to focus on what I could.

Tiwa: The open-ended nature of the work is the most difficult aspect of working at Dyno! The problems AAViators work on haven’t been solved before, so you have to think really hard about what will/won’t work. Thinking creatively about how to approach problems with no answer key is really hard (though the work wouldn’t be nearly as fun if it weren’t so challenging!)

Jeff: The most difficult aspect of working at Dyno was the lack of social interaction due to the COVID restrictions that forced many people in the company to work from home. There are a lot of cool people at Dyno that I wish I could talk more in a more casual context. I missed having the coffee chat breaks with employees and relaxed lunches. These are interactions that, from past experience, not only stimulate friendship and a sense of belonging, but also creativity and work efficiency. So it was difficult being surrounded by all of these driven people and not being able to get as close as I’d like.

Stephen: At Dyno, the ground truth about how good our AAVs are is quite expensive to determine and can take on the order of months. Instead, we have to figure out ways to validate our methods using proxies that hopefully reflect the actual results of experiments.

Max: There were many steep learning curves I had to overcome when I started at Dyno, which made it challenging for me to progress with my project at the start. Catching up with the literature and learning about the tools and techniques Dyno uses were certainly not trivial. However, I enjoyed the learning process and I had a lot of help to overcome these learning curves. Reaching out for help is something I could have done more often, and would have lowered some of these barriers.


How did you grow professionally while you were here?

Max: This internship has been an incredibly valuable experience for me professionally. Being a small company, everyone at Dyno (including interns!) has the opportunity to observe or participate in many aspects of the business. This includes listening in on partnership presentations or interview presentations for potential hires, as well as discussions regarding the company culture and business goals. I learned a lot about how the company as a whole operates. On the technical side of things, I learned a lot about the infrastructure and design of our pipelines, as well as explored new ideas that could be useful long after I leave. The level of involvement I have had in bigger design questions was very unique and extremely valuable.

Stephen: I’ve worked as a traditional software engineer, but this was my first time working full-time on machine learning in an industry setting, especially a biotech one. As a result, I in general learned a lot about how to build, validate, & share ML models in an industry setting. In particular, I spent a lot of time working on, thinking about, and learning from other Dyno team members validating our models in lieu of being able to get ground truth validation of their new predictions. I have a longer term goal of becoming the type of person who can reliably and productively think independently, and I feel that the Dyno DS/ML team members and environment moved me closer to that goal by giving me the time and support to seek solutions on my own and explore outside my comfort zone.

Stewy: I came into this summer having taken courses and done some projects involving machine learning, but I hadn’t worked on any real-world ML systems. Having the opportunity to do that this summer was hugely valuable. Knowing how to deal with data imbalances and spot silent training bugs, seeing through the hype and getting a feel for what works and what doesn’t – you don’t get these kinds of things from reading papers, only from working on real systems. I still have a lot to learn, but after this summer, I feel much more confident in my ability as a machine learning practitioner.

Jeff: I grew substantially in my ability to do research and make experimental decisions. Because I was given a large degree of independence, I was able to really stretch my creativity and navigate my own decision tree. Learning how to fail and fail quickly, to collaborate toward directions outside your expertise, and to brainstorm experiment design, these are all skills that I felt like I lacked in my undergraduate research experience. 

Tiwa: In addition to improving my machine learning skills, I grew professionally by chatting with people all over the company, cultivating my network of both data scientists and biologists. Nobody minded taking the time to speak to me about their role, background, and goals. Additionally, I attended internal career panels which gave me a better sense of the important nuances underpinning pursuing careers in ML/AI.


What’s your advice for people who are interested in interning at Dyno? 

Max: Working at Dyno can be a super fulfilling experience. Come ready to learn a lot, and also struggle a lot. Being independent is important to facilitate the learning process, but that doesn’t mean you shouldn’t ask for help often. Be ready to tackle hard problems where no one knows what the answer should be, but also have a lot of fun doing it. Make sure to reach out for help, ask questions, read, collaborate often, and have fun!

Tiwa: One piece of advice I would give is to always be ready to learn. The importance of this is clear when you’re stuck and you’re looking for feedback/advice, but it really applies in general when interning here. I think that a hunger to learn more is a big part of the culture at Dyno; the experience will be even more valuable if you’re always willing to ask questions and expose yourself to learning opportunities.

Stewy: Dyno is a great place to be. Every person I met really cared about me, and I will always appreciate that. But every person I met also really cared about the work they were doing, and I think that it’s important to share that excitement. If you are considering working at Dyno for the summer, I suggest you read up and learn about what the company is doing, get excited about it, and then hit me up!

If reading this post makes you excited about interning at a startup that aims to help millions of people live better through enabling gene therapy using tools at the cutting edge of machine learning and synthetic biology, consider applying through our careers page.

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