RESEARCHER (GENERAL)

MakerMaker.AI; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

MakerMaker.AI in San Francisco is looking for a Researcher to design and develop methods for autonomous research agents. You will explore complex ML research questions that evolve over time, collaborating with engineers to bring promising methods to production.

The ideal candidate has over 5 years of experience in ML, including published research in top conferences and a strong command of methods like RL and LLMs. This role requires comfort with ambiguity and a bias towards actionable research outcomes.

Qualifications

• 5+ years of hands-on research experience in industry or academia.
• Published research at NeurIPS, ICML, ICLR, COLM, RLC, or comparable venues.
• Experience in designing experiments and running them at scale.

Responsibilities

• Identify research questions that enhance agent capabilities.
• Design and run experiments end-to-end from framing to write-up.
• Develop methods across various ML domains.

Skills

Machine Learning research

Reinforcement Learning (RL)

Large Language Models (LLMs)

Strong written communication

PyTorch

Ambiguity navigation

Education

PhD in ML, statistics, computer science or adjacent

Tools

Jax

Job description

ABOUT THE COMPANY

We're building autonomous research agents for recursive self‑improvement (multi‑agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on‑site

ABOUT THE ROLE

As a Researcher on our team, you'll design experiments and develop methods that drive how our autonomous research agents make decisions. You'll work across the full ML research stack (problem formulation, method design, experimentation, analysis, write‑up) and you'll do it on problems that don't always have established benchmarks because we're inventing the workloads.

The work is open‑ended and concrete at the same time. Open‑ended because the research problems are constantly evolving and we don’t prescribe approaches. Concrete because the research questions are motivated by real‑world applications. Open‑ended because we don't have prescribed research directions; concrete because every experiment ties to something the agents will actually do. You'll have real autonomy (and the corresponding responsibility for choosing well).

WHAT YOU'LL DO

• Identify research questions that, when answered, would meaningfully change what our agents are capable of
• Design and run experiments end‑to‑end (from problem framing through method design, infrastructure, evaluation, and write‑up)
• Develop new methods spanning RL, LLMs, agentic systems, multi‑agent coordination, search, evaluation, or wherever the problem leads
• Work closely with engineers to take the most promising methods from research code into production
• Read deeply across the literature; bring useful work from outside in
• Help shape how the team picks problems

WHAT WE'RE LOOKING FOR

• Strong track record of ML research at the frontier: RL, LLMs, agentic ML, multi‑agent systems, evaluation, or adjacent
• 5+ years of hands‑on research experience in industry or academia
• Comfortable designing experiments and running them at scale, not just proposing them
• Strong written communication: you can summarize your research findings into actionable insights for next steps
• Fluent in PyTorch, Jax or equivalent; comfortable working with large‑scale training infrastructure
• Bias toward shipping research rather than handing it off
• Comfortable with ambiguity: many of our problems don't have a known right answer, and navigating that uncertainty is core to the role.
• Published research at NeurIPS, ICML, ICLR, COLM, RLC, or comparable venues

NICE TO HAVE

• PhD in ML, statistics, computer science, or adjacent
• Open‑source contributions to ML research infrastructure
• Experience with agentic systems, tool use, long‑horizon planning, or multi‑agent coordination

THIS ROLE IS PROBABLY NOT FOR YOU IF

• You want to focus on one specific benchmark and watch the metric tick up (our problems are broader and shift)
• You prefer more pure research that never touches a production system
• You'd rather work alone than share research taste openly with a small team