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Staff Software Engineer, AI/ML

  • 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

Building AI agents that take real actions is the easy part. Building agents that get better over time, that learn from feedback, correct mistakes and optimize toward outcomes researchers care about, is one of the hardest open problems in production AI. As a Staff AI/ML Engineer on Applied Research, you will own the technical direction for feedback-driven learning in IAMB.AI's agentic research systems: reward modeling from human reviewers' decisions, preference optimization, reinforcement learning, and the evaluation needed to know whether any of it works. This is a senior individual contributor role with broad scope: you set direction, run experiments at scale and close the loop between human signals and model behavior, shipping research into production.

Own the feedback learning roadmap

  • Define and carry out the applied research agenda for feedback-driven agentic AI, from reward modeling and preference optimization to online learning and human feedback loops.
  • Turn human reviewers' corrections, evaluation data and product signals into concrete training and optimization strategies.
  • Stay close to the frontier on RLHF, RLAIF, DPO, PPO, GRPO and related methods, and know when simpler approaches win.

Build production learning systems

  • Design and build learning loops that improve agent reasoning, planning, tool use and action over time.
  • Build evaluation that measures what matters: reasoning quality, instruction following, agreement with human reviewers, task success and safety, offline and online.
  • Run large-scale experiments that connect model changes to measurable improvements for researchers.

Provide technical leadership

  • Set technical direction across modeling, experimentation, evaluation design and production readiness, without needing management authority.
  • Work closely with product, engineering, design and research to move work from prototype to shipped capability.
  • Explain complex AI systems clearly to technical and non-technical people.

What you will bring

  • Research contributions through peer-reviewed publications, patents, open-source work, or demonstrated applied research impact in reinforcement learning, reward modeling, evaluation or recommendation systems.
  • 8+ years building production AI or ML systems: LLMs, generative AI, agentic systems, recommendation, search, personalization or applied research at scale.
  • Hands-on experience improving AI systems through reinforcement learning, reward modeling, fine-tuning, human feedback or preference optimization, with results you can point to.
  • A strong understanding of agentic AI: reasoning, planning, tool use, action, instruction following and self-correction.
  • Strong software engineering in Python and at least one production systems language.
  • The judgment to balance model quality, impact, latency, reliability, cost and maintainability, and to explain those trade-offs clearly.

Preferred qualifications

  • Experience with agent evaluation, offline and online experiments, and human feedback loops in production.
  • Direct experience with RLHF, RLAIF, DPO, PPO, GRPO or related optimization techniques.
  • Prior Staff, Senior Staff, Tech Lead or equivalent senior individual contributor experience.
  • A master's or PhD in computer science, machine learning, AI or a related field, or equivalent depth shown through industry work.
  • Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores or experimentation platforms.

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.