Technical Staff Member for Frontier AI Research

<p>This role focuses on enhancing model and system performance through rigorous evaluation, failure analysis, 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 translate into meaningful improvements in deployed systems. </p><strong>Key Responsibilities </strong><ul><li>Own research and evaluation initiatives end-to-end: problem framing, data design, quality calibration, and signal validation. </li><li>Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and pipelines optimized for downstream model performance. </li><li>Analyze model and system failures to identify root causes, edge cases, and opportunities for improvement. </li><li>Translate ambiguous, real-world behavior into structured evaluation frameworks and new data categories. </li><li>Collaborate with researchers and domain experts to calibrate quality and continuously raise the signal bar. </li><li>Iterate rapidly on evaluations, datasets, and feedback loops to enhance system performance. </li><li>Act as a quality gate: block claims, pause work, or force scope changes when signal strength or data integrity is insufficient. </li><li>Partner with cross-functional and client-facing teams to translate research progress into clear, credible narratives grounded in evidence. </li><li>Identify gaps in data or evaluation coverage and recommend where to invest, iterate, or stop based on learnings and impact. </li> </ul><strong>Qualifications </strong><ul><li>Strong judgment regarding research signal quality and readiness for externalization. </li><li>Experience designing ML-oriented datasets, evaluation frameworks, and QA processes. </li><li>Ability to translate messy, real-world system behavior into structured research and evaluation opportunities. </li><li>Comfort operating in ambiguity, with a bias toward ownership and decisive action. </li><li>Clear written and verbal communication skills, particularly when explaining tradeoffs, limitations, and signal strength to both technical and non-technical stakeholders. </li><li>Proven ability to work directly with experts during project kickoff, calibration, and iteration. </li><li>A systems-level mindset, focusing on improving end-to-end model or agent performance rather than isolated components. </li> </ul><strong>Preferred </strong><ul><li>Experience with reinforcement learning environments, simulators, or feedback-driven training systems. </li><li>Experience improving agentic systems or AI systems operating in real-world workflows. </li><li>Prior work embedded in applied research or production environments with direct impact on deployed systems. </li><li>Experience with evaluation design for complex or real-world tasks. </li><li>Familiarity with expert incentive design and engagement in high-stakes technical projects. </li> </ul><strong>Work Terms </strong><p>This is a full-time, remote position. </p><strong>Compensation </strong><p>The compensation range for this role is $600, 000 to $2, 000, 000 per year. </p><strong>Eligibility </strong><p>All employees must meet the eligibility requirements as outlined in company policies. </p>