Strong track record in ML research focusing on agents, RL, LLMs, planning, tool use, or multi-agent systems
5+ years of hands-on research experience in industry or academia
Experience designing and executing experiments at scale
Proven ability to build evaluation frameworks for non-benchmarked capabilities
Bias towards shipping research rather than handing it off
Strong written communication skills for concise result sharing
Comfortable with ambiguity in open-ended problem-solving
Responsibilities
Design methods to enhance agent planning and task decomposition
Develop coordination patterns for multi-agent collaboration
Build and maintain evaluation frameworks for open-ended tasks
Conduct rigorous experiments to assess agent performance
Collaborate with engineering teams to co-design agent architectures
Engage with relevant literature to integrate external insights
Share findings to foster team knowledge and direction
Benefits
Real autonomy and ownership in research
Opportunity to shape research direction within the team
Engagement with cutting-edge, open-ended problems
Collaborative environment with a focus on shared research taste
Potential for impactful contributions to the field of agentic ML
Full Job Description
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
