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Forward Deployed Applied Scientist I (Agentic AI)

  • Remote
  • Team: Forward Deployed Research Engineering
  • 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

Forward Deployed Research Engineering works where AI research, production deployment and real research teams meet. You will not sit in an isolated lab: you will embed with research groups, labs and co-author teams, and design, build and deploy the agentic workflows that take their projects from a question to a published paper on IAMB.AI.

What you will do

  • Architect production-ready agentic frameworks: multi-agent workflows, autonomous reasoning loops and tool-augmented LLM pipelines that solve real research problems, from search strategies to screening, extraction and synthesis.
  • Work directly with research teams: embed with principal investigators, lab leads and co-authors, and turn ambiguous research needs into reliable, well-evaluated agentic workflows.
  • Synthesize research and deployment: prototype new agentic approaches quickly (LangGraph, AutoGen or custom execution graphs) and harden them for production, with durable state and memory.
  • Drive reliability and evaluation: build reusable evaluation frameworks and guardrails that measure reasoning quality, agreement with human reviewers, execution safety, latency and cost.
  • Close the loop with the product: turn what you learn in the field into improvements to IAMB.AI's core platform.
  • Validate the platform as its first customer: use IAMB.AI's AI reviewers, autopilot, worker computers and evaluation tools on real projects, and bring architectural gaps and scaling bottlenecks straight to engineering and research.
  • Travel up to 30% for research team engagements, workshops, conferences and collaboration.

What you will bring

  • Research contributions through peer-reviewed publications, or demonstrated applied research impact in agentic AI, LLMs or evaluation.
  • 4+ years of hands-on experience in applied AI, machine learning or data science.
  • Expertise in multi-agent frameworks: multi-agent orchestration, tool use and function calling, MCP, structured outputs, dynamic planning and persistent memory.
  • Production Python and systems engineering: clean, production-ready Python (Pydantic, FastAPI, asyncio, PyTorch).
  • A customer-facing engineering mindset: empathy, crisp technical communication, and the ability to explain AI trade-offs to both engineers and research leads.
  • An owner's mindset: you think big and act quickly, with a strong sense of ownership over the experience of the teams you work with.

Preferred qualifications

  • Publications, patents, and knowledge of the latest research on LLMs, VLMs and agentic frameworks.
  • A PhD or master's degree in computer science, machine learning, AI or a related field.
  • Applied science experience with deep learning frameworks (PyTorch or TensorFlow), distributed training and agentic AI frameworks; a solid understanding of transformer and state space models.
  • The ability to translate complex research tasks into AI engineering solutions and to work directly with research leads.
  • Comfort in fast-moving environments, including tuning model workloads for different accelerators.
  • Experience working with model vendors or partners on benchmarking, optimization, fine-tuning or launch readiness.

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.