Machine Learning Research Lead, Security & Policy Research Lab

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale has launched a new team, the Scale AI Security and Policy Research Lab, to bridge the gap between AI researchers and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Scale is seeking a highly experienced, thoughtful, and mission-driven research lead to drive this team's research priorities to tackle the hardest problems in agent robustness, AI control protocols, and AI risk evaluations. Our research team will act as a central hub for generating the data and insights necessary to inform benchmarking and evaluations for AI models and systems, with a focus on how to utilize this research to inform secure, responsible, and innovative AI governance globally. We collaborate broadly across industry, the public sector, and academia and regularly publish our findings.

SPRL's research team is shaping the next generation of safety science for frontier AI models and works at the leading edge of frontier risk evaluations, agent robustness, and AI controls. Some of our current research includes:

• Designing and building harnesses to test AI models and systems (including agents) for dangerous capabilities such as hacking or exploiting security vulnerabilities;
• Conducting uplift research studies related to potentially dangerous chemical, biological, nuclear, or radiological (CBRN) AI-enabled uplift;
• Developing AI-assisted evaluation pipelines, where models help critique, grade, and explain outputs related to frontier risk (e.g. RLAIF, model-judging-model);
• Collaborating with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation.

• Lead a team of research scientists and engineers on foundational AI safety and security work in evaluation and robustness.
• Drive research initiatives on frameworks and benchmarks for frontier AI models, spanning reasoning, coding, multi-modal, and agentic behaviors.
• Design and advance scalable oversight methods, leveraging model-assisted evaluation, rubric-guided judgments, and recursive oversight.
• Collaborate with leading research labs across industry and academia.
• Publish research at top-tier venues and contribute to open-source benchmarking initiatives.
• Remain deeply engaged with the research community, both understanding trends and setting them.

• Track record of impactful research in machine learning, especially in generative AI, evaluation, or oversight.
• Significant experience leading ML research in academia or industry.
• Strong written and verbal communication skills for cross-functional collaboration.
• Experience building and mentoring teams of research scientists and engineers.
• Publications at major ML/AI conferences (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) and/or journals.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We do not ask LeetCode-style questions.