How would one pivot to AI research or engineering as a generalist SWE?
I'm thinking there are a few options:
- Get a role on an AI project/team (internal transfer), learn AI specific engineering expertise on the job (not research)
- Do part-time masters, land a role in AI research
- Self-learn, land a role in AI research
Discussion
(2 comments)- 25 months agoTech Lead/Manager at Meta, Pinterest, Kosei
In my experience, #1 is the best option (along with some combination of #3). If you're in a high-growth company, or one that values their employees like Google, there will be a way for you to switch into other parts of the company.
You have a much better chance of getting a good role within your company because people already know and trust your work. Once you've switched into a genAI team at Google, even if it's not perfect, you'll easily land the next AI role. The other benefit is that you typically keep your level/compensation with internal transfers, while the risk of down-leveling is higher if you switch companies.
Some additional thoughts on a masters:
- 25 months agoTech Lead @ Robinhood, Meta, Course Hero
#1 is by far the best, especially since you work at Google, a company that is renowned for both its AI/ML strength and superb team-switching culture. #2 would be a waste of time relatively given your situation. You can also invest in #3 as well to bolster your ramp-up/learning, but #1 is the main one. If you're doing #3, I highly recommend building some side projects: [Taro Top 10] Building Impressive Side Projects
Here's another great thread on how to transition and the overlap between SWE and MLE: "Software Engineer interested in ML - How would a transition work?"
I also recommend the playbook I wrote on career stack pivots here: "How to transition from back-end development to distributed systems?"
