AI Safety Researcher: Safety, Evaluation & Red-Teaming

Thinking Machines Lab Inc.; San Francisco (CA); On-site; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. in San Francisco is seeking a safety researcher to bridge research and hands-on engineering, focusing on making models safe and trustworthy.

You will explore how training shapes refusals and how to evaluate boundaries, designing experiments to inform model training and evaluation strategies.

Join a team that values rigorous analysis, data-driven evaluation, and responsible AI practices, with opportunities to influence real-world deployments.

Qualifications

• Bachelor’s degree or equivalent in Computer Science, Machine Learning, Physics, Mathematics, or related field with strong theoretical and empirical grounding.
• Background in AI safety research with hands-on experience in at least one safety area (RLHF/RLAIF, alignment, red-teaming, etc.).
• Proficiency in Python and familiarity with DL frameworks; comfortable debugging distributed training.

Responsibilities

• Work across data curation, safety-focused fine-tuning, evaluations, and red-teaming across the development stack.
• Design experiments to understand how training shapes model refusals and safe behavior.
• Develop safety evaluations and synthetic data to test boundaries and risk surfaces.
• Collaborate with teams to implement mitigations for safety failures and jailbreaks.

Skills

Python programming

Deep learning frameworks

Communication clarity

AI safety research background

Education

Bachelor’s degree or equivalent in CS/ML/Physics/Math

PhD in CS/ML/Physics/Math optional

Tools

PyTorch

TensorFlow

JAX

Job description

Thinking Machines Lab Inc. in San Francisco is seeking a safety researcher to bridge research and hands-on engineering, focusing on making models safe and trustworthy.

You will explore how training shapes refusals and how to evaluate boundaries, designing experiments to inform model training and evaluation strategies.

Join a team that values rigorous analysis, data-driven evaluation, and responsible AI practices, with opportunities to influence real-world deployments.