Research, Safety

The mission of Thinking Machines is to build AI that extends human will and judgment.

As a safety researcher, you'll work toward ensuring our models are safe and trustworthy. The role sits at the intersection of research and hands-on technical work, spanning the full development stack — from pre-training data curation to safety-focused fine-tuning, evaluations, and red-teaming. A central question is how models come to handle harmful or dual-use requests: what they learn from data, how training shapes where they refuse and where they engage, and what makes those boundaries reliable. You'll explore the science behind these behaviors and design experiments that inform how our models are trained and evaluated. What You’ll Do Build data filtering pipelines and quality classifiers to shape what models learn from pre-training corpora — and study how those early interventions affect downstream safety behavior. Apply post-training techniques — including RL from human and AI feedback and policy-based reasoning approaches — to shape how models handle harmful, sensitive, and dual-use requests. Design, build, and maintain safety evaluations, with particular focus on measuring model behavior on long-horizon and agentic tasks. Generate and curate synthetic data to train and evaluate models on refusal boundaries and safety-relevant behaviors. Red-team our models and products to surface failure modes, jailbreaks, and emergent risks before deployment — and design mitigations for what you find.

Required qualifications: Strong background in AI safety research, with hands-on experience in areas such as RLHF/RLAIF, alignment and preference modeling, deliberative alignment, safety evaluations, or red-teaming. Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales. Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply if you meet some but not all of these: Experience building evaluations for long-horizon, multi-step, or agentic tasks. Experience generating synthetic data at scale for training or evaluation. Experience with modern red-teaming/jailbreaking techniques. Research contributions in AI safety — publications, open-source evaluations, or public red-teaming work. Familiarity with the AI safety literature and current open problems (e.g., scalable oversight, reward hacking, jailbreak robustness). Logistics Location: This role is based in San Francisco, California.

Depending on background, skills and experience, the expected annual salary range for this position is [salary range] USD. Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.