Job Hunt as a PhD in AI

Some notes from Nathan Lambert on job hunting with a Ph.D. from Berkeley.

Job Process Reflections

High yield: Candidates from prestigious schools can experience high response rates to job applications, allowing more time for reflective interview preparation rather than application quantity.
Waiting game: Rejections are usually received before acceptances, suggesting a pattern of companies responding faster to non-preferred candidates; it's common and provides an opportunity to network and hone interview skills.
Networking value: The AI research community is small and offers the chance to meet high-quality contacts, adding long-term value beyond immediate job prospects.
Research scientist titles: These carry prestige and can affect career paths significantly, with companies like OpenAI offering more neutral titles like "Member of Technical Staff" that can have varied recruitment pipelines.
Postdoc consideration: A postdoc is suitable for some, opening doors especially in academia; however, for those with industrial aspirations, it's often better to proceed without unless a postdoc offers a uniquely supportive environment for growth.

The paper-writing research scientist interviews: all about evaluating your vision, your place in the broader research community, and ability to work with the team.
The product-focused research scientist interviews: mix between faculty search self-promotion and practical engineering questions, in reality they will do the same work as other engineers.
The machine learning engineer interviews: pushed more on practical problems. More machine learning fundamentals interviews (things like logistic regression, probability fundamentals, vector calculus, and math puzzles).
The robotics engineer interviews: fun systems questions. extremely specific to robotics, less time talking about ML or RL.

What do you want to work on when you show up? Who do you want to work with? If you had unlimited compute what would you work on?
What was a past project you worked on? What was the impact? What did you learn?
What styles of communication do you prefer? Do you work in teams? Do you value mentorship?

Coding Practice: Consistent coding practice, such as using LeetCode premium.
ML Background Interviews: While tricky, preparing for "ML Background" interviews (math tricks and basic ML tradeoffs) through coursework study can be advantageous.
Job Talk Preparation: Emphasize a strong narrative and vision in your job talk, focusing less on technical detail and more on engaging storytelling.
Research Agenda: Clearly outlining a research agenda with timelines and goals can clarify your process and intentions for starting new projects.
Saying No: Be willing to decline unreasonable demands from companies; most are flexible and aim for a positive candidate experience.
Interview Topics: Inquire about interview preparation topics; recruiters might provide specific areas to focus on, such as programming concepts or problem-solving domains.