There\u2019s a pervasive, mutual challenge in the job market today for people working in (or wanting to work in) the cutting edge of AI. On the hiring side, it often feels impossible to close, or even get interest from, the candidates you want. On the individual side, it quite often feels like the opportunity cost of your current job is extremely high \u2014 even if on paper the actual work and life you\u2019re living is extremely good \u2014 due to the crazy compensation figures.<\/p>\n
For established tech workers, the hiring process in AI can feel like a bit of a constant fog. For junior employees, it can feel like a bit of a wall.<\/p>\n
In my role as a bit of a hybrid research lead, individual contributor, and mentor, I spend a lot of time thinking about how to get the right people for me to work with and the right jobs for my mentees.<\/p>\n
The advice here is shaped by the urgency of the current moment in LLMs. These are hiring practices optimized for a timeline of relevance that may need revisiting every 1-2 years as the core technology changes \u2014 which may not be best for long-term investment in people, the industry, or yourself. I\u2019ve written separately<\/a> about the costs of this pace, and don\u2019t intend to carry this on indefinitely.<\/p>\n The most defining feature of hiring in this era is the complexity and pace of progress in language models. This creates two categories. For one, senior employees are much more covetable because they have more context of how to work in and steer complex systems over time. It takes a lot of perspective to understand the right direction for a library when your team can make vastly more progress on incremental features given AI agents. Without vision, the repositories can get locked with too many small additions. With powerful AI tools I expect the impact of senior employees to grow faster than adding junior members to the team could. <\/p>\n This view on the importance of key senior talent has been a recent swing, given my experiences and expectations for current and future AI agents<\/a>, respectively:<\/p>\n Every engineer needs to learn how to design systems. Every researcher needs to learn how to run a lab. Agents push the humans up the org chart.<\/p>\n On the other side, junior employees have to prove themselves in a different way. The number one defining trait I look for in a junior engineering employee is an almost fanatical obsession with making progress, both in personal understanding and in modeling performance. The only way to learn how the sausage gets made is to do it, and to catch up it takes a lot of hard work in a narrow area to cultivate ownership. With sufficient motivation, a junior employee can scale to impact quickly, but without it, it\u2019s almost replaceable with coding agents (or will be soon). This is very hard work and hard to recruit for. The best advice I have on finding these people is \u201cvibes,\u201d so I am looking for advice on how to find them too!<\/p>\n For one, when I brought Florian Brand<\/a> on to help follow open models for Interconnects, when I first chatted with him he literally said \u201csince ChatGPT came out I\u2019ve been fully obsessed with LLMs.\u201d You don\u2019t need to reinvent the wheel here \u2014 if it\u2019s honest, people notice.<\/p>\n For junior researchers, there\u2019s much more grace, but that\u2019s due to them working in an education institution first and foremost, instead of the understatedly brutal tech economy. A defining feature that creates success here is an obsession with backing up claims. So a new idea improves models, why? So our evaluation scores are higher, what does this look like in our harness? Speed of iteration follows from executing on this practice. Too many early career researchers try to build breadth of impact (e.g. collecting contributions on many projects) before clearly demonstrating, to themselves and their advisors, depth. The best researchers then bring both clarity of results and velocity in trying new ideas.<\/p>\n Working in academia today is therefore likely to be a more nurturing environment for junior talent, but it comes with even greater opportunity costs financially. I\u2019m regularly asked if one should leave a Ph.D. to get an actual job, and my decision criteria is fairly simple. If you\u2019re not looking to become a professor and have an offer to do modeling research at a frontier lab (Gemini, Anthropic, OpenAI is my list) then there\u2019s little reason to stick around and finish your Ph.D.<\/p>\n The little reason that keeps people often ends up being personal pride in doing something hard, which I respect. It\u2019s difficult to square these rather direct pieces of career advice with my other recommendations of choosing jobs based on the people, as you\u2019ll spend a ton of your life with them, more than the content of what you\u2019ll be doing. Choosing jobs based on people is one of the best ways to choose your job based on the so-called \u201cvibes.\u201d<\/p>\n Working in a frontier lab in product as an alternative to doing a Ph.D. is a path to get absorbed in the corporate machine and not stand out, reducing yourself to the standard tech career ladder. Part of what I feel like works so well for me<\/a>, and other people at Ai2, is having the winning combination of responsibility, public visibility, and execution in your work. There is something special for career progression that comes from working publicly, especially when the industry is so closed, where people often overestimate your technical abilities and output. Maybe this is just the goodwill that comes from open-source contributions paying you back.<\/p>\n