Thinking Machines Lab
Engineering Manager
San Francisco
H1B sponsorship available$400k-$500kDetected 55 days ago
Machine LearningPyTorchLogisticsSystems EngineeringCollaboration
About the role
- This role combines collaboration with hand-on work.
Responsibilities
- Lead and grow a team of senior and staff-level engineers, setting clear expectations and maintaining a high bar for execution.
- Own architecture, system design, and long-term technical direction for your team's systems, with emphasis on reliability and performance.
- Contribute directly to design reviews, prototyping, and debugging critical issues.
- Partner with researchers and product teams to define roadmaps and prioritize work.
Requirements
- Bachelor's degree or equivalent industry experience in computer science, engineering, or similar.
- 8+ years of experience building and scaling production systems, including system design and distributed systems.
- 3+ years of engineering management experience in high-growth environments.
Nice to have
- we encourage you to apply even if you don't meet all preferred qualifications, but at least some:
- Experience managing teams of senior or staff-level engineers.
- Background in infrastructure, systems engineering, or developer productivity.
- Familiarity with AI/ML systems, data infrastructure, or high-performance computing.
Skills
- Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence.
Compensation
- Depending on background, skills and experience, the expected annual salary range for this position is $400,000 - $500,000 USD.
Benefits
- Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Visa & Work Authorization
- 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.
- We sponsor visas.
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This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.