Research, Safety
Thinking Machines Lab is hiring a Research, Safety for a remote role open to applicants in United States. It pays $350k-$475k a year and Level rates it ; you can apply on Thinking Machines Lab.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- The posting lists this role as remote.
- Thinking Machines Lab mentions visa sponsorship in this posting.
- Tools and skills the posting cites include PyTorch, TensorFlow and JAX.
- Level lists 57 other open roles at Thinking Machines Lab.
AI in this role
Research and implement safety measures, evaluations, and alignment techniques for frontier AI models.
The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
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. You'll explore the science behind these behaviors and design experiments that inform how our models are trained and evaluated.
What You’ll Do
We are hiring across the entire development stack — from pre-training data curation to safety-focused fine-tuning, evaluations, and red-teaming. During project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented safety researchers with the teams where they'll have the greatest impact and growth potential.
Here are example areas you may contribute to depending on your area of expertise and interest:
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.
Skills and Qualifications
Required qualifications:
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.
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).
PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.
Logistics
Location: This role is based in San Francisco, California.
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 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.
Benefits: 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.
How we rate this
Research, Safety at Thinking Machines Lab rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where red teaming was part of your work. What did you do?
- Tell me about a project where model evaluation was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, AI Safety, Machine learning, Red Teaming, and Model Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
Get your CV reviewed before you apply
Send your CV and the role you want next. Your review arrives by email within 3 days.
Get new remote research jobs (Builds AI ●●●●) by email
One email a week, each job rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free.
Similar roles
Research roles that build AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step