Technical Staff Member for Frontier AI Systems

Lead technical ownership at the intersection of research, data, and deployed AI systems to improve model and agent performance through rigorous evaluation, failure analysis, and iterative development. This hands-on position drives end-to-end research signal quality, ensuring experiments produce clean, defensible results that translate into real-world system improvements.

micro1 is an AI data lab that converts diverse subject matter expertise into high-quality training data, evaluations, and feedback loops for frontier models and AI agents. The company builds a vetted global expert network and a platform to scale expert contributions across domains such as finance, healthcare, and engineering.

• Own research and evaluation initiatives from problem framing through data design, quality calibration, and signal validation.
• Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentive structures, and pipelines optimized for downstream model performance.
• Analyze model and system failures to identify root causes, edge cases, and opportunities for improvement.
• Translate ambiguous, real-world behavior into structured evaluation frameworks and new data categories.
• Work closely with researchers, domain experts, and operators to calibrate quality early and continuously raise the signal bar.
• Iterate rapidly on evaluations, datasets, and feedback loops to drive measurable system improvements.
• Act as a quality gate, including blocking claims, pausing work, or forcing scope changes when signal strength or data integrity is insufficient.
• Partner with cross-functional and client-facing teams to translate research progress into clear, credible narratives grounded in evidence.
• Identify gaps in data or evaluation coverage and recommend where to invest, iterate, or stop based on learnings and impact.

Required

• Demonstrated strong judgment around research signal quality, including knowing when work is or is not ready to be externalized.
• Experience designing ML-oriented datasets, evaluation frameworks, and QA processes.
• Ability to translate messy, real-world system behavior into structured research and evaluation 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 during project kickoff, calibration, and iteration.
• A systems-level mindset, focused on improving end-to-end model or agent performance rather than isolated components.
• Core skills expected, including research, signal judgment, ML-oriented data design, ops-to-research translation, and familiarity with reinforcement learning environments.

Preferred

• Experience with reinforcement learning environments, simulators, or feedback-driven training systems.
• Experience improving agentic systems or AI systems that operate in real-world workflows.
• Prior work embedded in applied research or production environments with direct impact on deployed systems.
• Experience designing evaluations for complex or real-world tasks.
• Familiarity with expert incentive design and engagement in high-stakes technical projects.

• Employment type, full-time.
• Location, remote.
• Role is a hands-on technical owner working across research, data, and operations to improve deployed AI systems.

• Target total compensation range stated in the listing, $600, 000 to $2, 000, 000 per year.
• National pay range for base salary, $180, 000 to $320, 000 per year.

• This is a full-time remote employee role. Candidates must be able to accept full-time employment terms for a remote position.