Research Scientist, Safety Post Training

About the role

Scale Labs, Research Scientist — Safety Post Training. 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 Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

Responsibilities

• Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties.
• Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations.
• Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices.

Requirements

• Commitment to promoting safe, secure, and trustworthy AI deployments as frontier AI capabilities advance.
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems in research or product development.
• Strong written and verbal communication skills for cross‑functional teamwork.

Qualifications

• Experience with mechanistic interpretability, probing, or other techniques for understanding model internals.
• Familiarity with red‑teaming or adversarial evaluation of post‑trained models.
• Experience studying failure modes introduced or masked by post‑training, such as reward hacking, sycophancy, or alignment faking.

Skills

• RLHF
• DPO

Pay

Base salary range for this full‑time position in San Francisco, New York, and Seattle: $216,000 — $270,000 USD. Compensation packages also include equity and benefits, determined during the interview process based on location, skills, experience, and interview performance.

Benefits

• Comprehensive health, dental, and vision coverage
• Retirement benefits
• Learning and development stipend
• Generous paid time off (PTO)
• Potential commuter stipend