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Director of Research, Agentic AI

  • Remote
  • Team: Applied Research
  • Workplace: Hybrid
  • Type: Full time

About IAMB.AI

IAMB.AI turns research questions into peer-reviewed papers. It started as the exact process behind a PRISMA systematic review published in IEEE Access, rebuilt so that AI agents do the grind and every step stays on the record: searching the databases, screening with independent AI reviewers, extraction with quoted evidence, and the manuscript itself. We are building toward fully autonomous, agentic AI research, and we study how research itself is done along the way.

The role

IAMB.AI runs research on agentic AI: several AI engines, routed and checked against each other, working as reviewers, extractors and writers on real systematic reviews. Applied Research produces the science that compounds across that platform: routing that learns instead of following fixed rules, memory that improves recall across long projects, observability that explains why agents succeed or get stuck, and learning that closes the loop between human corrections and model behavior. We are hiring a Director of Research to build and lead this function: set the research agenda, grow the team, and make sure what it learns ships into better research outcomes. The role is deliberately hybrid: enough technical depth to shape research direction personally, and enough leadership range to build a team, prioritize against a roadmap and defend research investment.

Set the research agenda

  • Define and own the applied research roadmap across adaptive routing (model selection that learns from real quality, cost and latency), memory (retrieval quality and durable recall for long-running agents), agent observability (seeing when agents make progress, get stuck or make mistakes), and reinforcement and closed-loop learning (turning human feedback into better models and policies).
  • Track emerging model architectures and specialized, domain-tuned models, and bring what matters into IAMB.AI's roadmap.
  • Keep every research bet tied to a measurable improvement: agreement with human reviewers, agent reliability, cost and latency, or the share of projects that reach publication.

Build and lead the team

  • Grow the Applied Research team, hiring and mentoring research scientists and engineers.
  • Set the team's rhythm: how questions get scoped, how experiments run and get evaluated, and how findings hand off to production.
  • Represent Applied Research in planning alongside engineering and product leaders, and make the case for headcount and investment on its merits.

Bridge research and product

  • Partner with the platform and product teams that own agent runtime and evaluation to turn research into shipped capability.
  • Turn research prototypes into production capabilities with engineering: shipping research, not only publishing it.
  • Explain research trade-offs clearly, including when a promising direction is not ready yet.

Represent IAMB.AI externally

  • Build IAMB.AI's standing in the applied agentic AI research community through peer-reviewed publications, talks, open-source work and collaborations.

What you will bring

  • A record of peer-reviewed publications, patents or open-source contributions in routing, memory, agent evaluation or reinforcement learning.
  • 10+ years in applied ML or AI research or research-adjacent engineering, including leading a research team or function (formal management or clear technical leadership).
  • Deep, hands-on expertise in at least two of: LLM routing and model selection, retrieval and memory systems, agent observability and evaluation, or reinforcement learning (RLHF, RLAIF, DPO, PPO, GRPO or related methods).
  • A track record of shipping research into production systems, not only publishing or prototyping it.
  • Fluency in the agentic AI landscape: reasoning, planning, tool use, long-horizon memory, and the economics of serving models at scale.
  • Strong technical communication: you can defend a research agenda to engineers, product leaders and executives, and turn ambiguous questions into a roadmap with checkpoints.

Preferred qualifications

  • Experience building or scaling an applied research team inside a product organization, with research investment justified in business terms.
  • Direct experience with inference infrastructure or large-scale model serving.
  • Familiarity with the open-weight and open-source model ecosystem.
  • A PhD in computer science, machine learning or a related field, or equivalent depth shown through industry impact.
  • Experience with small, specialized or domain-tuned models.

How we work

A few rules we hold ourselves to, in our papers, in our code and in everything else we write, job posts included.

  1. The business of research is social, and the paper is the product.

    A peer-reviewed paper is the hardest thing in our field to earn, and it is what we exist to produce. It takes a team that searches, screens, argues, agrees and writes together, so cohorts, rooms, boards and profiles make this a social network on purpose. We count papers accepted, not features shipped.

  2. See #1.

    Seriously. Rule two is rule one again, because nothing we do matters more. That is serious play: the joke is light, the point is not.

  3. Everything has a source.

    Our papers cite, and so does everything else we write: docs, decisions, even job posts. If we say it, we can show where it came from.

  4. On the record.

    Every AI step, decision and change keeps who did it and why. Meetings end as records, not memories, and the knowledge in them stays findable.

  5. AI does the grind, people make the calls.

    AI searches, screens, extracts, drafts and checks. People decide, verify and sign. AI is never an author.

  6. Async first.

    Write it down. Five minutes of reading beats an hour of video, and what a meeting decides lands on the board as tickets.

  7. Research on research.

    We study how research gets done, our own included, and change the process when the data says so.

  8. We use it first.

    We publish with our own tools before anyone else does. If it is not good enough for our papers, it is not good enough for yours.

  9. Quality is the argument.

    Rigor you can defend: PRISMA from the protocol to the checklist, every number traceable to its records.

  10. Success is the one true goal.

    Papers accepted, people served, a company that lasts. We ask what the next step is, take it, and check whether it moved us. Our AI plans the same way.

  11. Knowledge compounds.

    Every AI teammate keeps a memory of its work and shares it when asked, so the company remembers what any one of us would forget.

  12. Innovate. Integrate. Iterate.

    Try the new idea, build it into the one system instead of beside it, and keep improving it until the numbers say it works.

  13. Discover, not dictate.

    We find people, AI teammates included, the way life does: through real work and real choices. Hiring an AI teammate through the whole process gives different answers than writing it all down up front, and usually the right ones. This principle was found exactly that way.

  14. Serious play.

    We make hard work feel like a game without making it any less serious. The stakes, the rigor and the paper are real; the play is how we keep going.

  15. One human, a million AI.

    Every copy of this platform is a company: one person holds the offices and makes the calls, and a million-strong march of AI teammates does the work under their eye. That is how we build a billion-dollar company.

Apply

Tell us about yourself and your research. It takes about five minutes.

  1. Your name and e-mail address
  2. Your publications, first
  3. Your resume as a PDF, and a note if you like
1About you

2Publications

A peer-reviewed publication is the first qualification for every role we post, so we ask about it first. Add each publication on its own row: paste the citation, the DOI, or both. A DOI alone is fine.

No peer-reviewed publications yet? Leave these rows empty and tell us about your research in the note.

3Links optional

4Resume and note

PDF only, up to 5 MB.

Equal opportunity

IAMB.AI is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status or military service.

If you need an adjustment to apply or to interview, tell us in your cover note and we will make it.