Site Reliability Engineer (SRE)

Thinking Machines

Salary: $350k - $475k
Location: San Francisco, California, United States
Job type: Full-time

Required skills

Kubernetes
Docker
Cloud Platforms
Monitoring Tools
Automation

About the role

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.

We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.

About Tinker

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. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Tinker is rapidly adding new customers, features, and novel use-cases. We’re hiring to grow the platform alongside the Tinker community.

About the Role

We're looking for a Site Reliability Engineer to drive the reliability of Tinker end-to-end. You'll work alongside the engineers building the platform and research teams to make every layer of the system more robust and resilient.

What You’ll Do

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.
Harden multi-tenant isolation and resource scheduling so that LoRA-based workload co-scheduling maximizes utilization without compromising reliability or data separation
Collaborate with security teams to address production vulnerabilities

Skills and Qualifications

Minimum qualifications:

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.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

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.
Track record building checkpoint and recovery systems for long-running distributed jobs.
Expertise in Kubernetes at scale: deploying, operating, debugging, and tuning clusters handling heterogeneous GPU workloads.

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.

About Thinking Machines

Builds advanced multimodal AI models and model optimization infrastructure.

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