micro1 connects domain experts to the development of frontier AI models. Real-world expertise is turned into training data, evaluations, and feedback loops that improve how models perform. AI labs and enterprises use micro1 to train models and build reliable AI agents through advanced evaluations and reinforcement learning environments. Experts contribute directly to how AI systems learn, reason, and perform across domains like finance, healthcare, engineering, and more. Our platform identifies and vets top talent through an AI recruiter, enabling high-quality contributions at scale.

Our goal is to enable 1 billion people to do meaningful work by applying their expertise to AI. We’ve raised $40M+ in funding, and our AI recruiter has powered over 1 million AI-led interviews as our global network of experts grows into the human intelligence layer for AI.

micro1 is a data engine that helps AI labs train foundational models and enterprises build AI agents. We provide frontier evaluations and reinforcement learning environments used to improve LLM capabilities, as well as contextual evaluations used to monitor and improve AI agents in enterprise settings. Our data engine includes an AI recruiter agent that sources and vets domain experts, a data platform that enables rapid production of high-quality training data, and a pipeline performance system that ensures both quality and velocity.

The Role

We’re hiring a Member of Technical Staff (MTS) to act as a technical owner operating at the intersection of research, data, and real-world AI systems. This is a hands-on role focused on improving model and system performance through rigorous evaluation, failure analysis, and iterative development.

You’ll work closely with researchers, domain experts, and operators to ensure that experimental work produces clean, defensible research signal—and that this signal translates into meaningful improvements in deployed systems.

What You’ll Do

1. Own research and evaluation initiatives end-to-end: problem framing, data design, quality calibration, and signal validation.
2. Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and pipelines optimized for downstream model performance.
3. Analyze model and system failures to identify root causes, edge cases, and opportunities for improvement.
4. Translate ambiguous, real-world behavior into structured evaluation frameworks and new data categories.
5. Work closely with researchers and domain experts to calibrate quality early and continuously raise the signal bar.
6. Iterate rapidly on evaluations, datasets, and feedback loops to improve system performance.
7. Act as a quality gate: block claims, pause work, or force scope changes when signal strength or data integrity is insufficient.
8. Partner with cross-functional and client-facing teams to translate research progress into clear, credible narratives grounded in evidence.
9. Identify gaps in data or evaluation coverage and recommend where to invest, iterate, or stop based on learnings and impact.

What We’re Looking For

1. Strong judgment around research signal quality and when work is (or is not) ready to be externalized.
2. Experience designing ML-oriented datasets, evaluation frameworks, and QA processes.
3. Ability to translate messy, real-world system behavior into structured research and evaluation opportunities.
4. Comfort operating in ambiguity, with a bias toward ownership and decisive action.
5. Clear written and verbal communication, especially when explaining tradeoffs, limitations, and signal strength to technical and non-technical stakeholders.
6. Proven ability to work directly with experts during project kickoff, calibration, and iteration.
7. A systems-level mindset, with interest in improving end-to-end model or agent performance rather than isolated components.

Preferred

1. Experience with reinforcement learning environments, simulators, or feedback-driven training systems.
2. Experience improving agentic systems or AI systems operating in real-world workflows.
3. Prior work embedded in applied research or production environments with direct impact on deployed systems.
4. Experience with evaluation design for complex or real-world tasks.
5. Familiarity with expert incentive design and engagement in high-stakes technical projects.

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