--- title: 'RESEARCHER, AGENTS FOR AUTOMATED DISCOVERY at MakerMaker' canonical: 'https://feeny.ai/job/researcher-agents-for-automated-discovery-makermaker-san-francisco-a726565wtck7' type: 'job' last_seen: '2026-09-08' --- # RESEARCHER, AGENTS FOR AUTOMATED DISCOVERY at MakerMaker - **Company:** MakerMaker - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-05-18 - **Last confirmed live:** 2026-09-08 - **Apply:** https://jobs.ashbyhq.com/makermaker.ai/d20781b4-6126-4f2e-b52b-ed64d922209e ## 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 ## About MakerMaker ## Company Overview - **One-liner**: MakerMaker.AI builds AI agents that autonomously build other AI agents, enabling the rapid creation and deployment of agentic systems. - **Entity Type**: Private (Seed stage) - **Headquarters**: San Francisco, California, United States - **Founded**: 2024 - **Founders**: Dhaval Adjodah (CEO & Co-Founder), Owen He (CTO & Co-Founder) ## Core Business - Primary industry/industries: Artificial Intelligence, Agentic AI, Enterprise Software - Target customers: B2B, Enterprise (engineering and product teams looking to automate AI agent development) - Mission or purpose statement: To build agents that build agents, accelerating the creation of autonomous AI systems. ## Products & Services - **MakerMaker.AI Platform**: A proprietary AI system that autonomously designs, builds, and deploys other AI agents. The platform is designed to reduce the time and cost of agent development, enabling teams to create sophisticated agentic workflows without extensive manual coding. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed - **Key Metric**: Total Funding — Not publicly disclosed (Seed stage) - **Notable Investors/Partners**: Not publicly disclosed - **Growth Signals**: Headcount grew +300% YoY (from ~1 to 7 employees); team includes talent from Google DeepMind, Schmidt Sciences, Intel, and Insilico Medicine; presence in Canada, United States, and Poland; co-founders published an op-ed in The Washington Post on DeepSeek and open-source AI. ## Competitive Advantages - **Founder expertise**: CEO Dhaval Adjodah and CTO Owen He have deep backgrounds in AI research and engineering, with connections to top AI labs. - **Unique value proposition**: The "agents building agents" approach could dramatically lower the barrier to creating custom AI agents, a rapidly growing market. - **Early mover in agentic AI**: MakerMaker is positioned at the cutting edge of the autonomous AI agent trend, which is a major focus for the industry in 2024-2025. ## Strategic Focus - Scaling the platform and expanding the engineering team - Recruiting top ML and engineering talent to accelerate product development - Building a strong presence in the San Francisco Bay Area AI ecosystem - Likely focused on proving product-market fit and securing a Series A round ## Why Work Here - **Culture**: Small, high-impact team (currently ~7 people) with a strong technical focus — 62% of the workforce is in technical roles. - **Remote/Hybrid/Office**: The team is distributed across Canada, the US, and Poland, but the company is hiring for a full-time role in San Francisco, suggesting a preference for in-person or hybrid collaboration at the HQ. - **Notable perks**: Opportunity to work on one of the most ambitious problems in AI (building agents that build agents); direct collaboration with the founding team; steep learning curve and high ownership. - **Engineering culture**: Likely research-heavy, fast-paced, and focused on pushing the boundaries of what AI agents can do. The talent pool includes alumni from Google DeepMind and Intel, indicating a high bar for technical skill. ## Sources 1. [makermaker.ai](https://makermaker.ai/) 2. [LinkedIn - MakerMaker.AI](https://www.linkedin.com/company/makermaker) 3. [RocketReach - MakerMaker.AI](https://rocketreach.co/makermakerai-profile_b6fdbc3fc646e3f0) 4. [AIJobs.com - MakerMaker AI Jobs](https://www.aijobs.com/companies/makermaker-7261448) 5. 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