Thinking Machines Lab
Research, Safety
San Francisco
H1B sponsorship available$350k-$475kDetected 53 days ago
PythonMachine LearningDeep LearningTensorFlowPyTorchLogisticsResearchCommunication
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odds of building a lasting career here
59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
16 transfer filings in the last year, covering 16 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- 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.
- 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.
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.
- Design, build, and maintain safety evaluations, with particular focus on measuring model behavior on long-horizon and agentic tasks.
- Red-team our models and products to surface failure modes, jailbreaks, and emergent risks before deployment, and design mitigations for what you find.
Requirements
- 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.
- Comfort debugging distributed training and writing code that scales.
- Clarity in communication, an ability to explain complex technical concepts in writing.
- Experience generating synthetic data at scale for training or evaluation.
- Experience with modern red-teaming/jailbreaking techniques.
- Familiarity with the AI safety literature and current open problems (e.g., scalable oversight, reward hacking, jailbreak robustness).
Nice to have
- we encourage you to apply if you meet some but not all of these:
Compensation
- Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Benefits
- Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
- This flexible approach allows us to match talented safety researchers with the teams where they'll have the greatest impact and growth potential.
- Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
Company info
- We are hiring across the entire development stack - from pre-training data curation to safety-focused fine-tuning, evaluations, and red-teaming.
Visa & Work Authorization
- 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.
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This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.