Technical Staff Member for Frontier AI Systems

This role focuses on enhancing model and system performance through rigorous evaluation and iterative development. As a Member of Technical Staff, you will operate at the intersection of research, data, and real-world AI systems, ensuring that experimental work yields clean, defensible research signals that lead to meaningful improvements in deployed systems. Key Responsibilities Own research and evaluation 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. 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. Collaborate closely with researchers and domain experts to calibrate quality and continuously raise the signal bar. Iterate rapidly on evaluations, datasets, and feedback loops to enhance system performance. Act as a quality gate: block claims, pause work, or enforce 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. Qualifications Strong judgment regarding research signal quality and readiness for externalization. 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 skills, especially in explaining tradeoffs, limitations, and signal strength to both technical and non-technical stakeholders. Proven ability to work directly with experts during project kickoff, calibration, and iteration. Work Terms Full-time position, remote work environment. Compensation Annual compensation ranges from $600, 000 to $2, 000, 000. Eligibility Open to candidates with relevant experience and skills in the field.