Thinking Machines Lab
Site Reliability Engineer, Production
San Francisco
Stay score
odds of building a lasting career here
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 →H-1B wage level
the lottery is wage-weighted — each level is one more entry
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
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.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- Tinker https://thinkingmachines.ai/tinker/ is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs - opening access to capabilities that have previously been concentrated in a handful of labs.
- Tinker is rapidly adding new customers, features, and novel use-cases.
- We're hiring to grow the platform alongside the Tinker community.
Responsibilities
- Define and own end-to-end reliability, from CI/CD flows to production observability and incident response.
- Develop appropriate Service Level Objectives for distributed training systems, balancing job completion reliability and scheduling latency with development velocity.
- Design and implement monitoring and observability across the full training path.
- Drive incident response for Tinker platform issues, ensuring rapid recovery, thorough incident reviews, and systematic improvements that prevent recurrence.
- Collaborate with security teams to address production vulnerabilities
- We believe the future worth building is human, and we're hiring people who want to build it.
Requirements
- Bachelor's degree or equivalent experience in computer science, engineering, or similar.
- Experience in distributed systems, cloud infrastructure, or site reliability engineering.
- Proficiency writing software to solve reliability problems, including building tooling and automation.
- Experience with production incident response, postmortems, and systematic reliability improvement.
- Strong communication skills and track record of coordination across engineering and research teams.
Nice to have
- Deep experience operating production cloud services at scale (e.g., public cloud platforms, internal cloud services)
- Background in distributed training frameworks and how infrastructure failures surface in training behavior.
- Expertise in Kubernetes at scale: deploying, operating, debugging, and tuning clusters handling heterogeneous GPU workloads.
- 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.
Compensation
- Depending on background, skills and experience, the expected annual salary range for this position is $300,000- $350,000 USD.
Benefits
- Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Company info
- 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.
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.
This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.