Member of Technical Staff, Frontier AI

Drive measurable improvements in frontier AI systems by owning the connection between research, data design, evaluation, and real-world system performance. This hands-on technical role turns experimental findings and operational behavior into rigorous, defensible signals that guide deployed AI and agent improvements. Key Responsibilities

• Lead research and evaluation initiatives from problem framing through data design, quality calibration, and signal validation.
• Design ML-focused data systems, including task definitions, annotation schemas, rubrics, incentives, and pipelines that support downstream model performance.
• Investigate model and system failures to identify root causes, edge cases, and improvement opportunities.
• Convert ambiguous real-world behavior into structured evaluation frameworks and new data categories.
• Partner with researchers, domain experts, and operators to calibrate quality early, strengthen research signal, and translate findings into deployed-system improvements.
• Iterate quickly on evaluations, datasets, and feedback loops to improve performance.
• Serve as a quality gate by challenging claims, pausing work, or changing scope when data integrity or signal strength is insufficient.
• Work with cross-functional and client-facing teams to communicate research progress through clear, evidence-based narratives.
• Identify gaps in data and evaluation coverage, then recommend where to invest, iterate, or stop based on impact and learnings.

• Strong judgment about research-signal quality and readiness for external use.
• Experience designing ML-oriented datasets, evaluation frameworks, and quality-assurance processes.
• Ability to turn messy real-world system behavior into structured research and evaluation opportunities.
• Comfort operating in ambiguity, with decisive ownership.
• Clear written and verbal communication for explaining tradeoffs, limitations, and signal strength to technical and non-technical stakeholders.
• Demonstrated ability to work directly with experts during project kickoff, calibration, and iteration.
• A systems-level mindset focused on end-to-end model or agent performance.

• Reinforcement learning environments, simulators, or feedback-driven training systems.
• Improving agentic AI systems or AI systems used in real-world workflows.
• Applied research or production work with direct impact on deployed systems.
• Evaluation design for complex or real-world tasks.
• Expert incentive design and engagement in high-stakes technical projects.

• Full-time, remote role.

• Stated annual compensation range: $600, 000 to $2, 000, 000.
• Stated national base-salary range for this full-time position: $180, 000 to $320, 000 annually.