Member of Technical Staff, Research Lab

About micro1

micro1 builds the human data and evaluation infrastructure that powers modern AI systems. Their platform is utilized by leading AI labs and Fortune 10 companies to source, assess, and deploy elite human expertise directly into model training, evaluation, and feedback loops. The company combines applied AI, large-scale human data, and rigorous evaluation frameworks to enhance model performance in production, turning expert human judgment into high-signal datasets and continuously improving AI systems.

The Role: Member of Technical Staff, Research Lab

micro1 is seeking a Member of Technical Staff (MTS) to serve as a technical owner within its Research Labs. This is a hands-on position that bridges research, data design, and real-world deployment. The successful candidate will ensure that experimental work yields clean, defensible research signals that translate into tangible, customer-relevant outcomes.

What You’ll Do

• Own research initiatives from end-to-end, covering 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.
• Collaborate directly with domain experts and operations teams to establish early quality and continually elevate the signal bar.
• Transform operational failures, ambiguities, and edge cases into new research directions and data categories.
• Act as a critical quality gate, prepared to block claims, pause work, or enforce scope adjustments when signal strength or data integrity is insufficient.
• Partner with go-to-market and client-facing teams to articulate research progress into clear, credible narratives supported by evidence.
• Identify data gaps and provide recommendations on where to invest, iterate, or cease efforts based on insights and commercial relevance.

What micro1 Is Looking For

• Strong judgment regarding research signal quality and readiness for externalization.
• Experience in designing ML-oriented datasets, including annotation frameworks and QA processes.
• Ability to translate complex operational realities into structured research opportunities.
• Comfort operating in ambiguous environments, with a strong bias towards ownership and decisive action.
• Excellent written and verbal communication skills, particularly when explaining tradeoffs, limitations, and signal strength to diverse stakeholders.
• Proven capability to work directly with experts, especially during project kickoff, calibration, and iterative phases.

Nice to Have

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

Key skills/competency

• Research Design
• ML Data Systems
• Data Quality
• Signal Validation
• Annotation Frameworks
• Stakeholder Communication
• AI Systems
• Reinforcement Learning
• Applied Research
• Problem Framing