I help leaders hire AI researchers and builders by making the role, the evidence, and the opportunity specific to the work ahead. My work brings together hands-on search, partnership with hiring leaders, and the perspective of building AI tools myself. I am doing this work directly at kAIgentic, including searches for core researchers as well as the people who turn AI capability into products and systems.
When a team is hiring in AI, I want to understand what that means for the work ahead. A core research role, an AI product role, and an engineering role using AI extensively need different evidence. The work may call for someone to advance a technical question, turn research into a product, or build the systems that make it useful in practice.
That can be a single critical hire, a new team, or capability being built across a function. I work with leaders to connect the search to that wider picture, make the assessment specific, and give candidates a clear sense of the work they would own.
Hiring AI talent starts with the work the team needs done
A strong search moves from defining the capability to setting a new colleague up to succeed:
- Clarify the hiring need: agree what the team needs to become capable of, what is already there, and what contribution would raise Talent Density.
- Find relevant researchers and builders: follow the problems people have worked on, including adjacent talent pools and less obvious career paths.
- Make the problem worth exploring: connect the work with people whose interests and experience give them a reason to care, then help the team explain the contribution they could make.
- Help both sides decide: develop the interviewers’ lines of questioning, examine the evidence together, and make the opportunity and its constraints clear to the candidate.
- Set up success and learn: carry the agreed contribution, authority, and support into the handover, then learn from what the person is able to do after joining.
The role determines the evidence worth seeking
The hiring leader and I need a shared view of the contribution: the problem, the user, the technical constraints, and the decisions the person will own. That helps distinguish where deep model expertise matters, where product or engineering judgment matters, and where someone needs to learn quickly across disciplines.
The environment matters too. Access to data, compute, specialist colleagues, evaluation infrastructure, time to investigate, and decision authority shapes what someone can achieve. I bring those conditions into the brief and discuss them with candidates, so the team can make an honest promise about the work it will support.
Core research hiring starts with an unanswered technical question
For a core AI research role, I start with the question the team needs to answer. Is the work about advancing a model, developing a method, investigating an unresolved technical constraint, or creating the evidence that will shape a product or research direction? That determines the research depth, experimental freedom, and collaboration the role needs.
Doctoral or master’s-level training can be an important signal, especially for roles that demand deep technical foundations. It does not settle the assessment. I want to understand the person’s intellectual contribution: the problem they chose, the hypothesis they formed, the method they used, the evidence they trusted, and what changed because of the work.
This makes the candidate conversation specific. We can explore how someone handles ambiguous results, challenges an assumption, interprets a failed experiment, and works with colleagues who need to carry the research forward. Papers, technical writing, open-source work, and prior research are useful starting points. The conversation helps us examine the judgment behind them. Qualified research colleagues assess the technical depth and the strength of the findings.
Search for work that matches the problem
My search approach maps the problem to organisations, adjacent talent pools, and people doing relevant work. For core researchers, useful starting points can include research groups, technical papers, open-source contributions, and work on adjacent problems. For AI builders, they can include shipped products, technical writing, and accounts of how a system was built or improved.
Each is a lead to examine. Public visibility is uneven; confidential work may leave little public trace. I use the conversation to understand what the person actually owned, which constraints they faced, and what they can appropriately share.
Sourcing Compass reflects the broader search discipline: making target organisations, adjacent pools, and related titles visible before outreach begins.
An opportunity must be worth the candidate’s attention
Part of my work is helping leaders explain why the problem is worth someone’s attention. Before approaching candidates, I want to understand who it affects, what makes it difficult, what has already been tried, and what solving it would make possible. That gives us a more useful starting point than a list of technologies or responsibilities.
Then I think about who might find that work compelling. Someone drawn to research may want to explore the unanswered technical question and the room to experiment. A product-minded engineer may be more interested in the user problem, the feedback loop, and the path to putting something useful into people’s hands. An experienced platform builder may want to understand the reliability challenges and the scope to influence architecture. Those are starting points for a conversation; I want to hear what actually interests the person.
That connection also shapes where I search. The people most interested in a problem may have encountered it in another industry, contributed to an adjacent tool, or written thoughtfully about a constraint the team is facing. Their work gives me a specific reason to reach out and a way to explain why this conversation might be worth their time.
For example, a brief about building an AI assistant becomes more tangible when we can explain whose work it supports, where current answers fall short, and what the person would have the authority to change. For someone interested in evaluation, the open question may be how to tell whether the system is useful in messy real-world conditions. For someone focused on adoption, it may be how to fit it into a workflow people already rely on.
I want the excitement to come from the work itself: a meaningful problem, thoughtful colleagues, and a contribution the person can see themselves making. We talk openly about the constraints too, including data access, resources, decision authority, and what is still uncertain. Meeting the people they would work with helps a candidate test that picture and form their own view.
This carries through the search. As I learn what matters to the candidate, I help the hiring team make the next conversation more relevant. The aim is a shared understanding of why the opportunity is worth pursuing and what both sides would need for it to work.
That is part of the wider Art & Craft of Recruiting: understanding the business, recognising the right audience for its problems, and helping both sides make an informed choice.
Assess contribution through the work itself
Examine the research contribution
For a research hire, I follow one piece of work closely. The questions are different from a production assessment:
- Problem: what was uncertain or unsolved, and why did it matter?
- Approach: what hypothesis, method, or experimental design did the person develop or materially shape?
- Evidence: how did they decide what the results showed, what comparison or control tested the conclusion, and what did the evidence leave unresolved?
- Judgment: where did they change direction, abandon a line of enquiry, or defend a conclusion under challenge?
- Contribution: what did they personally advance, and how did that work inform the team around them?
The aim is to understand the quality of the research judgment, the depth of the contribution, and the conditions in which the person will do their best work.
Follow the proof from launch to learning
For a role that requires production AI experience, I want to understand one system in depth. I follow the story through:
- Use: who relied on it and what work it helped them do.
- Ownership: what the person designed, built, or decided themselves.
- Operation: what failed after launch, how they diagnosed it, and what they changed.
- Measurement: how they knew it worked, what they compared it with, and what the measure missed.
- Constraints: how data access, security, customer requirements, cost, or reliability changed the design.
The role determines which evidence is essential and how current the experience needs to be. Using AI tools is useful context; a production-building role needs evidence of building and operating the system itself. Specialist depth needs the right specialist assessor.
A precise account of a failure can be particularly useful. It connects personal responsibility, diagnosis, and learning in a way that a polished demonstration alone cannot establish.
Look for a reasoned view that can evolve
Coachability matters alongside the capability needed today. I look for someone who can explain a decision, examine a challenge to it, and revise their view when better evidence changes the answer. They should also be able to explain why an objection does not change their reasoning.
An interview can explore that through a real design decision: what information was available, what would have changed the choice, and how the person responds to a relevant new constraint. The useful evidence is the reasoning and the change it produces.
That opens a second question: how will this person grow as the work changes? The immediate requirements should be clear and limited to what the role truly needs. Learning, judgment under ambiguity, and the ability to improve the team’s thinking deserve their own examination.
Structure the interviews and feedback
I work with the hiring team to agree what each conversation needs to establish: technical depth, personal contribution, judgment, or learning. Interviewers prepare relevant lines of questioning against the same role standard, with room to follow what the candidate brings.
Feedback records the evidence, the interviewer’s assessment, and the unanswered questions before we discuss it together. In the debrief, we examine where our interpretations differ and what would resolve the uncertainty. An area a round did not cover needs follow-up; it tells us nothing yet about the candidate’s capability.
That is how I connect structured interviews and feedback to a hiring decision the team can explain. If the role standard changes, we revisit the assessments affected by it.
AI supports recruiting when the evidence remains inspectable
Finding AI researchers and builders is connected to building AI-enabled recruiting systems. Interview Evaluator supports interviewer development; RishiOS applies assessment criteria and checks recorded evidence. The people responsible for a hire remain accountable for the decision.
Research and synthesis need source checks. AI-generated claims about a candidate cannot establish their capability. This discipline also runs through Raising the Bar.
For the wider workflow, explore the TA Operating System or read how I rebuilt hiring around repeatable steps and human judgment.