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About the Role

As a safety researcher, you will work towards ensuring our models are safe and trustworthy, sitting at the intersection of research and hands-on technical work. You will explore the science behind how models handle harmful or dual-use requests and design experiments to inform model training and evaluation.

Responsibilities

• 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 models and products to surface failure modes, jailbreaks, and emergent risks before deployment, and design mitigations for what is found.

Requirements

• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Background in AI safety research, with hands-on experience in at least one area of safety, 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.
• Clarity in communication, an ability to explain complex technical concepts in writing.
• 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).
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Skills

• Python
• Deep learning frameworks (PyTorch, TensorFlow, JAX)
• AI safety research
• RLHF/RLAIF
• Alignment and preference modeling
• Deliberative alignment
• Safety evaluations
• Red-teaming
• Communication
• Technical writing

Location

• San Francisco, California

Work Type

• Full-time

Experience Level

• Mid-level
• Senior

Education Level

• Bachelor's degree
• PhD

Salary/Compensations

• $350,000 - $475,000 USD

Benefits

• Health benefits
• Dental benefits
• Vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support
• Visa sponsorship

About the Company

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

Equal Opportunity

• 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.