micro1
(Frontier AI) Member of Technical Staff
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Member of Technical Staff (Frontier AI)
Job Type: Full-time Location: Remote
About micro1
micro1 builds the human data and evaluation infrastructure that powers modern AI systems. Our platform is used by frontier AI labs and Fortune 100 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
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- 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
- Pipelines optimized for downstream model performance
- Collaborate directly with domain experts and operations teams to:
- Calibrate early quality standards
- Continuously improve and raise the signal bar
- Convert operational challenges—such as failures, ambiguity, and edge cases—into new research directions and data categories.
- Act as a quality gate:
- Block claims, pause work, or suggest scope changes when signal strength or data integrity is insufficient.
- Partner with go-to-market and client-facing teams:
- Translate research progress into clear, credible narratives grounded in evidence
- Identify data gaps
- Recommend where to invest, iterate, or stop based on learnings and commercial relevance.
What We’re Looking For
You should bring:
- Strong judgment around research signal quality and the ability to determine when work is (or isn’t) ready for externalisation.
- Experience designing ML-oriented datasets, including:
- Annotation frameworks
- Quality assurance (QA) processes
- The ability to translate messy operational realities into structured research opportunities.
- Comfort operating in ambiguity, with a bias toward ownership and decisive action.
- Clear written and verbal communication, particularly when:
- Explaining trade-offs
- Highlighting limitations
- Describing signal strength to both technical and non-technical stakeholders.
- A proven ability to work directly with experts, especially in:
- Project kickoffs
- Calibration phases
- Iterations
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Nice to Have
- Experience with:
- Reinforcement learning environments
- Simulators
- 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 impact on:
- Sales
- Client outcomes
- Deployment.
- Familiarity with:
- Expert incentive design
- Engagement in high-stakes technical projects.
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