Member of Technical Staff, Frontier AI | $100-$130/hr Remote
Overview
This role is for a technical owner who lives at the intersection of research, data, and real-world AI systems. You'll drive rigorous evaluation, failure analysis, and iterative development to improve model and system performance. Working alongside researchers and domain experts, you'll ensure experimental work produces clean, defensible signal that translates into meaningful production improvements.
What You'll Do9
- 1Take full ownership of research and evaluation initiatives — from framing the problem to designing data, calibrating quality, and validating signal.
- 2Build ML-oriented data systems including task definitions, annotation schemas, rubrics, incentive structures, and pipelines optimized for downstream model performance.
- 3Analyze model and system failures to pinpoint root causes, edge cases, and actionable improvement opportunities.
- 4Translate ambiguous real-world behavior into structured evaluation frameworks and new data categories.
- 5Partner with researchers and domain experts early to calibrate quality and continuously raise the bar on research signal.
- 6Iterate rapidly on evaluations, datasets, and feedback loops to drive measurable system improvements.
- 7Serve as a quality gate — block claims, pause work, or force scope changes when signal strength or data integrity is insufficient.
- 8Collaborate with cross-functional and client-facing teams to turn research progress into clear, evidence-based narratives.
- 9Identify gaps in data or evaluation coverage and recommend where to invest, iterate, or stop based on learnings and impact.
Requirements7
- 1Sharp instincts for assessing research signal quality and knowing when results are (or aren't) ready to be externalized.
- 2Hands-on experience designing ML-focused datasets, evaluation frameworks, and QA processes that improve model behavior.
- 3Skill at turning messy, real-world system behavior into structured research questions and evaluation opportunities.
- 4Comfort operating in ambiguity with a bias toward ownership and decisive action.
- 5Excellent written and verbal communication — able to explain trade-offs, limitations, and signal strength to both technical and non-technical stakeholders.
- 6Proven ability to work hands-on with domain experts during project kickoff, calibration, and iteration.
- 7A systems-level mindset that cares about end-to-end model or agent performance rather than isolated components.
Who Should Apply
This role is ideal for someone who thrives at the intersection of research, data, and production AI. You're the kind of person who loves digging into why a model failed, designing rigorous experiments to diagnose issues, and then building the data and evaluation infrastructure to prevent future failures. You're comfortable with ambiguity, take ownership of outcomes, and can communicate complex trade-offs clearly to diverse stakeholders. If you've worked with reinforcement learning environments, agentic systems, or applied research that directly impacted deployed models, you'll find this role especially rewarding.
Salary Insight
The base salary for this full-time remote position is $180,000–$320,000, plus equity and performance-based bonuses. The company also offers comprehensive benefits including up to 100% reimbursement for health-insurance premiums, paid time off, and a 401(k) plan with company match.
Required Skills
Application Tip
When applying, include a brief case study of a time you designed an evaluation framework that uncovered a critical failure mode in an AI system. Describe how you defined metrics, validated signal quality, and what impact your work had on the final model performance.
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