Technical Staff Member for Frontier AI [Remote]

In this role, you will serve as a technical owner at the intersection of research, data, and real-world AI systems, focusing on enhancing model and system performance through rigorous evaluation, failure analysis, and iterative development. Your work will directly contribute to the advancement of foundational models and AI agents, ensuring that experimental efforts yield clean, defensible research signals that translate into significant improvements in deployed systems. Key Responsibilities

• Own research and evaluation initiatives end-to-end, including problem framing, data design, quality calibration, and signal validation.
• Design ML-oriented data systems, encompassing 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, blocking claims, pausing work, or enforcing 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 areas for investment, iteration, or cessation based on learnings and impact.

• Strong judgment regarding research signal quality and readiness for externalization.
• Experience designing ML-oriented datasets, evaluation frameworks, and QA processes.
• Ability to convert messy, real-world system behavior into structured research and evaluation opportunities.
• Comfort operating in ambiguity, demonstrating ownership and decisive action.
• Excellent written and verbal communication skills, particularly 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.
• A systems-level mindset, focusing on improving end-to-end model or agent performance rather than isolated components.

Full-time, remote position.

Annual salary ranging from $600, 000 to $2, 000, 000.

This position is open to candidates who meet the specified qualifications and can work remotely.