# Site Reliability Engineer (SRE)

**Company:** [Thinking Machines Lab](https://hotfix.jobs/companies/thinking-machines-lab)
**Location:** San Francisco, CA
**Role:** DevOps / SRE
**Salary:** $350k – $475k/yr
**Skills:** Kubernetes, Distributed Systems, Cloud Infrastructure, CI/CD, Observability, Incident Response, SLOs, Monitoring, Gpu Workloads, Automation Tooling
**Posted:** 2026-05-04

> Site Reliability Engineer drives end-to-end reliability for AI fine-tuning platform Tinker, including SLOs, monitoring, incident response, and multi-tenant GPU scheduling. Requires distributed systems experience, software proficiency for reliability, and production incident handling.

## Job Description

## 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:**

- 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.

## Compensation

Expected annual salary range: $350,000 – $475,000 USD.

## Benefits

Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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