RESEARCHER, AGENTS FOR AUTOMATED DISCOVERY

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

Job summary

MakerMaker.AI is seeking a researcher for autonomous systems focused on multi-agent machine learning. You will design methods and frameworks to improve agent capabilities and lead rigorous experiments in an open-ended research environment.

The ideal candidate has over 5 years of research experience in machine learning, particularly in areas like reinforcement learning, planning, and multi-agent systems. Strong written communication, as well as comfort with ambiguity and experimentation, is essential.

Qualifications

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

Responsibilities

• Design methods for agents to improve planning and problem decomposition.
• Develop multi-agent coordination patterns and evaluation frameworks.
• Run rigorous experiments to characterize research outcomes.

Skills

ML research

agents

reinforcement learning (RL)

planning

tool use

multi-agent systems

experiment design

evaluation frameworks

strong written communication

comfort with ambiguity

Education

PhD in ML, statistics, CS, or adjacent

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

You'll be researching the agents at the core of our work: multi-agent systems that conduct automated machine learning research and discovery. You'll design how these agents plan, decompose problems, choose what to try next, evaluate their own outputs, and recover from mistakes. This is a deeply open-ended research role. The benchmarks for agents that do real research don't exist yet, and inventing them is part of the job. You'll move between method design, careful experimentation, building evaluation frameworks, and shipping into production. Real autonomy, real ownership, and the corresponding responsibility for choosing well.

What You'll Do

• Design methods that improve how our agents plan, decompose tasks, use tools, manage context, and recover from failures across long-horizon research workflows.
• Develop multi-agent coordination patterns: how multiple agents share context, divide labor, supervise each other, and combine their outputs.
• Build and maintain evaluation frameworks for agent capability on open-ended tasks (the kind where the right answer isn't pre-specified).
• Run rigorous experiments to characterize what works, what doesn’t, and why: controls, ablations, statistical significance.
• Co‑design agent architectures with engineering teammates; ship the most promising methods into production.
• Read deeply across the agentic ML, planning, RL, and tool‑use literature; bring useful work from outside in.
• Share findings internally so the rest of the team builds on them.
• Help shape research direction across the team: agentic research taste compounds when discussed openly.

What We’re Looking For

• Strong track record of ML research with focus on agents, RL, LLMs, planning, tool use, or multi‑agent systems.
• 5+ years of hands‑on research experience in industry or academia.
• Comfort designing experiments and running them end‑to‑end at scale.
• Track record of building evaluation frameworks for capabilities that aren’t easily benchmarked.
• Bias toward shipping research, not handing it off.
• Strong written communication: you can compress a result into a paragraph that changes what someone else does next.
• Comfort with ambiguity: open‑ended problems without fixed benchmarks are the work, not a frustration.
• Published research at NeurIPS, ICML, ICLR, COLM, RLC, or comparable venues.

Nice to Have

• PhD in ML, statistics, CS, or adjacent.
• Published research on agentic systems, tool use, long‑horizon planning, multi‑agent coordination, or self‑improvement methods.
• Open‑source contributions in the agentic ML ecosystem (coding agents, research assistants, autonomous workflows).
• Experience with reasoning models, chain‑of‑thought / scratchpad methods, or supervised fine‑tuning for agentic behaviors.
• Background in evaluation methodology for capabilities that don’t have established benchmarks.

This Role Is Probably Not For You If

• You want to focus on a single stable benchmark: our agents work on open‑ended problems and the targets shift.
• You prefer to keep research paper‑only; these agents need to actually work. You’d rather work alone than share research taste openly with a small team.