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September 3, 2018
Why I Left Amazon
Friday August 17, 2018 was my last day at Amazon Go as an Applied Research Scientist II. The following Monday August 20th, 2018 was my first day at my new job as Senior Machine Learning Engineer at Root.ai. Deciding to leave Amazon was not an easy decision, and I wanted to share my personal reasons for leaving Amazon, both for myself for the future and for others in similar situations.
There are many reasons to leave a job, but ultimately it boils down to one thing: are you running FROM something or running TO something? For me, the decision to leave Amazon was not because of anything at Amazon, but because of the opportunity I was given a chance to run towards. I was definitely running TOWARDS my new job at Root.
What I did at Amazon
I spent a total of 2.5 years at Amazon. I joined after working for almost a year at an ultimately unsuccessful startup. At the time, I wanted to cut my teeth at research, and a big company like Amazon was going to be a good place to take a risk on doing research; a startup simply didn’t have the luxury of taking a risk on a new engineer eager to do research. I was also looking for mentorship and a professional network - I wanted to see how things could be done well. So the leap of faith between me and Amazon was mutual; I was given the title Applied Research Scientist (after a few months of asking nicely) despite not having a PhD, and in return I was expected to do world-class research.
My primary responsibility was to invent new algorithms, techniques, and processes for understanding the world around us. As an applied research scientist (as opposed to just a plain old research scientist) I was also expected to take these new algorithms and apply them to problems faced by Amazon, which often meant collaborating with engineers to put the algorithms into production code. In practice, you really could only do one of those two things: you were either the researcher who couldn’t write code, or the coder who wasn’t great at research. My strength is adapting to the needs of the situation as they arise, and my strength to flow between doing and researching mode was not given a chance to flourish.
In my time at Amazon, I succeeded at my two primary goals: getting mentorship (check) and learning how research works at a big company (check). In the process, I have made professional connections that I will carry with me for years to come, and on the research side I got an ArXiV paper in the process.
When I first took the job at Amazon, I did not know whether I would be the kind of person who climbs the corporate ladder and makes a corporate career at one place. Most of my peers from MIT did NOT follow this path, and honestly, that made me want to try it even more. My peer role models put startups and “working for yourself” on a pedestal, but I wanted to see for myself why that was the case. At 22 years old after my Master’s, I just didn’t know enough about the world to know what I wanted. So I did what any engineer would do: I experimented. In Amazon’s terms, I took a bias for action and went for it. I took the corporate job to see what it was like, learn what it means to do research in a corporate setting, and get mentorship for the future.
The offer I got from Amazon was very tempting: secret project out of a research group in the Boston area to work in a mixed hardware / software research problem with engineers and leaders at all levels. The pay was good, the benefits were better than the non-existent benefits I had at the startup before, and it hit my top-2 goal wish list. Besides, I had a good gut feeling about my would-be boss. And we all know that that’s often the most important thing. However, there were two big downsides:
- I would have to commute 50+ minute by car one way. I didn’t own a car (easily fixable), and that was a LOT of lost time. I was not sure how important this was to me at the time.
- I did not know what the project was. I did not find out what we were working on (back then it wasn’t even called Amazon Go) until my first day on the job.
The first issue, the commute, I thought would be outweighed by all the benefits of working at Amazon. For the better part of 2.5 years, I was right about that.
The second issue, the project, I knew I was taking a risk. My previous job (at Ditto Labs) had been in image and video analytics, which I was not passionate about. In contrast, before my MEng I had worked at MEET, where the mission and the core of what we were doing informed everything we were doing. Comparing those two experiences, at MEET I was excited to come to work every day, even when it was hard (there was literally a war towards the end of my stay in Israel). At Ditto, when things got tough, it was hard to find a foundation to fall back on. In hindsight, I should have known this would eventually be an issue.
On my first day at Amazon, I found out we were revolutionizing shopping, and my hopes sank a little bit. I was simply not passionate about optimizing or improving shopping. From day one, this nagged at me. However, it didn’t rear it’s ugly head until I contemplated leaving.
Throughout my time at Amazon, I focused on learning the most I could while adding the most value that I could. I took on projects that I thought were interesting, even proposing my own project that eventually led to the ArXiV paper, despite my lack of passion about physical retail. I still felt like I was adding value to the company and the group every day through my work. My focus was really on learning the most I could about what kind of work excited me, and it didn’t matter what problems I worked on to achieve that goal.
When a recruiter contacted me out of the blue about a robotics opportunity in the farming sector, I was more excited than I should have been. The nagging feeling about not working on something I believed in stood up in full force. I was reminded of my work with MEET years ago, when I felt excited to be coming to work every day not because of the work but because of the mission and the long-term vision. With Root, I was excited by the prospect of being on board with a larger vision of improving the world.
Leaving Amazon was not running away from a “bad opportunity” at Amazon, by no means. It was running towards an opportunity where I could be at my best, to a company with a mission that excited me. At Root, I could be both an engineer and a researcher (I didn’t have to choose or check a box), and I could do it fluidly while creating tangible, move-the-needle value from day one.
Root and Amazon could not be more different. But then again, I was approaching the two jobs at different points in my life, and I was a different person than I was 2.5 years ago. At Root, I was employee number 5 (if you count the co-founders). In my group at Amazon, I was employee number 13 (but by the time I left, I was the 6-th most senior group member, because of attrition). But overall, what I really was doing was running towards an opportunity that hit better with my career trajectory and my strength.
To summarize my reasons for leaving:
- I was running towards an opportunity where I believed in the mission and long-term vision of the company.
- In my new role I was fluidly moving between research and engineering, without being pigeon-holed into a single role.
- In my new role I am responsible for making company-wide recommendations for computer vision and machine learning. My choices would directly impact the company’s future.
- My commute was shortened from 50+ minutes by car to 10 minutes by bike.
- If I were to found my own company one day, being closer to the founders / venture capital money was a valuable career move.
- I was ready to move from the “learn a lot” phase to the “do a lot” and “make a lot of mistakes” phase of my career.
- In Amazonian terms, I was ready to step up my ownership game to the next level.
My reasons for leaving, like many before me, were personal. Your own mileage may vary. If anyone is in a similar predicament and wants to get in touch to discuss career trajectories, please feel free to get in touch.
All Posts about Miscellaneous
2018 September 3 -- Why I Left Amazon
2018 August 25 -- Blogging with Jupyter for Learning Julia Part 2
2018 August 17 -- Favorite Podcasts 2017-2018
2017 March 18 -- Blogging with Jupyter for Learning Julia
2017 February 15 -- Favorite Podcasts 2016-2017
2015 September 8 -- Installing Zim Wiki on OSX
2015 February 5 -- Word Frequency in the King James Bible
2014 February 13 -- A Fictional Middle East
2013 June 6 -- MIT Graduation Cap
2011 April 28 -- Playing Zork on Linux