Threat Research Engineer
You'll embed within a research team to build and improve the systems they depend on, including RL training infrastructure, data pipelines, sandboxing, and agent scaffolding. You need strong Python skills, end-to-end project leadership experience, and solid engineering fundamentals including debugging at scale. A bachelor's degree or equivalent in Computer Science, Machine Learning, or related fields is required. The role is based in San Francisco and pays $350,000–$475,000 annually.
Written from this posting by Neural Jobs AI. The full description is below.
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.
This role is responsible for building and strengthening the engineering foundations that our post-training research teams depend on. You'll embed within a research team, and build or improve the systems required for the team to succeed. Our research teams are small, and the role carries a corresponding degree of autonomy and responsibility.
Embed within a research team to build, harden, and improve the systems and infrastructure required for the team to succeed.
Design, build, and operate infrastructure research teams depend on, including RL training systems, sandboxing, data pipelines, and agent scaffolding.
Minimum qualifications:
Experience leading projects end to end, working in large fast-moving codebases, and writing code others have depended and built on.
Strong proficiency in Python and strong engineering fundamentals, with experience debugging systems that fail intermittently and at scale.
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.
Strong autonomous drive to progress towards the team’s goals with an ownership mindset.
Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but preferably some:
Experience building or iterating with sandboxed or containerized execution environments at a large scale.
Experience in a role where the team's priorities set yours, and a track record of success in such a role.
Familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
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 350000-475000 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.
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Thinking Machines Lab is an AI research and product company developing advanced models and tools that can adapt to individual needs. Its work focuses on making powerful AI more understandable, customizable, multimodal, and useful for collaboration with people. The company combines frontier research with products for model use and customization, including tools that let developers work with open-weight models. Thinking Machines Lab states a broader goal of giving more people access to the knowledge and capabilities required to shape AI for their own applications.
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Our estimate — this employer did not publish a salaryOur estimate, not the employer’s. Worked out from the middle half of 86 comparable roles on Neural Jobs that did publish a salary, in the same field, country and experience band. The real figure for this job may be different.
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