There’s 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 — even if on paper the actual work and life you’re living is extremely good — due to the crazy compensation figures.

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.

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.

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 — which may not be best for long-term investment in people, the industry, or yourself. I’ve written separately about the costs of this pace, and don’t intend to carry this on indefinitely.

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.

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, respectively:

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.

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’s 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 “vibes,” so I am looking for advice on how to find them too!

For one, when I brought Florian Brand on to help follow open models for Interconnects, when I first chatted with him he literally said “since ChatGPT came out I’ve been fully obsessed with LLMs.” You don’t need to reinvent the wheel here — if it’s honest, people notice.

For junior researchers, there’s much more grace, but that’s 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.

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’m regularly asked if one should leave a Ph.D. to get an actual job, and my decision criteria is fairly simple. If you’re 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’s little reason to stick around and finish your Ph.D.

The little reason that keeps people often ends up being personal pride in doing something hard, which I respect. It’s difficult to square these rather direct pieces of career advice with my other recommendations of choosing jobs based on the people, as you’ll spend a ton of your life with them, more than the content of what you’ll be doing. Choosing jobs based on people is one of the best ways to choose your job based on the so-called “vibes.”

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, 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.

If you go to a closed lab, visibility is almost always not possible, so you rely on responsibility and execution. It doesn’t matter if you execute if you’re doing great work on a product or model that no one ever touches. Being in the core group matters.

This then all comes back to finding the people hiring pipeline.

There are many imperfect signals out there, both positive and negative. For individuals building their portfolio, it’s imperative to avoid negative signals because the competition for hiring is so high. A small but clear negative signal is a junior researcher being a middle author on too many papers. Just say no, it helps you.

The positive signals are messier, but still doable. It’s been said that you can tell someone is a genius by reading one Tweet from them, and I agree with this. The written word is still an incredibly effective and underutilized communication form. One excellent blog post can signify real, rare understanding. The opposite holds true for AI slop. One AI slop blog post will kill your application.

The other paths I often advise people who reach out asking how to establish a career in AI are open-source code contributions or open research groups (e.g. EluetherAI). I’ve seen many more success cases on the former, in open-source code. Still, it’s remarkably rare, because A) most people don’t have the hardware to add meaningful code to these popular LLM repositories and B) most people don’t stick with it long enough. Getting to the point of making meaningful contributions historically has been very hard.

Doing open-source AI contributions could be a bit easier in the age of coding agents, as a lot of the limiting factors today are just bandwidth in implementing long todo lists of features, but standing out amid the sea of AI slop PRs and Issues will be hard. That’ll take class, creativity, humanity, and patience. So, to be able to run some tiny models on a $4000 DGX Spark is an investment, but it’s at least somewhat doable to iterate on meaningful code contributions to things like HuggingFace’s ML libraries (I’ve been writing and sharing a lot about how I’m using the DGX Spark to iterate on our codebases at Ai2).

Back to the arc of hiring, the above focused on traits, but the final piece of the puzzle is alignment. The first question to ask is “is this person good?” The second question is, “will this person thrive here?” Every organization has different constraints, but especially in small teams, the second question defines your culture. In a startup, if you grow too fast you definitely lose control of your culture. This isn’t to say that the company won’t have a strong or useful culture, it’s to say you can’t steer it. The culture of an organization is the byproduct of how all the individuals interact. You do not want to roll the dice here.

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Personally, I’m working on building out a few more spots in a core post-training methods team at Ai2. Post-training recipes have gotten very complicated, and we’re working on making them easier to run while doing research on fundamentals such as post-training data mixing and scaling laws. To be a little vague, getting the post-training recipes done for both Olmo 3 and Olmo 2 was... very hard on the team. At the same time, post-training hasn’t gotten much more open, so hiring through it and doing the hard work is the only way.

Ideally I would hire one engineer and one researcher, both fairly senior, meaning at least having a Ph.D. or a similar number of years working in technology. Junior engineers with some experience and the aforementioned obsession would definitely work.

This callout serves as a good lesson for hiring. It is intentional that people should self-filter for this, no one likes when you way overreach on selling yourself for a job. I also intentionally make people find my email for thi...