Member of Technical Staff (Research Lab)

About Us

micro1 builds the human data and evaluation infrastructure that powers modern AI systems. Our platform is used by frontier AI labs and Fortune 10 companies to source, assess, and deploy elite human expertise directly into model training, evaluation, and feedback loops. We combine applied AI, large-scale human data, and rigorous evaluation frameworks to improve model performance in production. From our AI recruiter and intelligence platform to internal data quality and research tooling, micro1 turns expert human judgment into high-signal datasets, measurable outcomes, and continuously improving AI systems.

The Role

We’re hiring a Member of Technical Staff (MTS) to operate as a technical owner inside our Research Labs. This is a hands-on role at the boundary of research, data design, and real-world deployment. You’ll be responsible for ensuring that experimental work produces clean, defensible research signal and that this signal translates into customer-relevant outcomes.

What You’ll Do

Own research initiatives end-to-end: problem framing, data design, quality calibration, and signal validation. Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and pipelines optimized for downstream model performance. Work directly with domain experts and operations teams to calibrate early quality and continuously raise the signal bar. Convert operational failures, ambiguity, and edge cases into new research directions and data categories. Act as a quality gate: block claims, pause work, or force scope changes when signal strength or data integrity is insufficient. Partner with go-to-market and client-facing teams to translate research progress into clear, credible narratives grounded in evidence. Identify data gaps and recommend where to invest, iterate, or stop based on learnings and commercial relevance.

What We’re Looking For

Strong judgment around research signal quality and when work is (or is not) ready to be externalized. Experience designing ML-oriented datasets, including annotation frameworks and QA processes. Ability to translate messy operational reality into structured research opportunities. Comfort operating in ambiguity, with a bias toward ownership and decisive action. Clear written and verbal communication, especially when explaining tradeoffs, limitations, and signal strength to technical and non-technical stakeholders. Proven ability to work directly with experts, especially during project kickoff, calibration, and iteration.

Nice to Have

Experience with reinforcement learning environments, simulators, or feedback-driven training setups. Prior work embedded within an R&D or applied research lab shipping customer-facing outputs. Ownership of research efforts with direct sales, client, or deployment impact. Familiarity with expert incentive design and engagement in high-stakes technical projects.