ML Training Infra SRE — Fast, Reliable RL Pipelines

Thinking Machines Lab; San Francisco (CA); On-site; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health benefits

Dental benefits

Vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines Lab in San Francisco, CA is hiring a Site Reliability Engineer to own the reliability and performance of post-training and RL training runs. You will work side by side with researchers, diagnose failures in real time, and build tooling to improve automation and resilience across clusters and pipelines.

The role emphasizes production engineering for model training, with on-call rotations and a focus on high-availability infrastructure and observability.

Qualifications

• 4+ years of experience as a production engineer, site reliability engineer, or infrastructure engineer operating large-scale distributed systems in production.
• Track record debugging complex failures across distributed systems — networking, hardware, kernel, or scheduler issues.
• Strong software engineering skills in Python and/or Go/C++, with the judgment to know when to script a fix versus build a system.
• Solid grounding in Linux systems internals and networking fundamentals.
• Comfortable owning production systems, including participating in on-call rotations.

Responsibilities

• Own the reliability, performance, and uptime of large-scale post-training and RL training jobs, from launch through completion.
• Partner directly with research teams during active model runs, embedding with them to unblock training and speed up iteration.
• Debug failures across the full stack — accelerators, networking, storage, schedulers, and training frameworks — and drive issues to root cause.
• Build monitoring, alerting, and automated recovery so runs self-heal or fail fast instead of silently stalling.
• Improve checkpointing, fault tolerance, and job scheduling so hardware failures cost minutes, not days of compute.
• Build internal tools that reduce toil and improve cluster utilization across post-training and RL workloads.
• Participate in an on-call rotation supporting production model runs.
• Write postmortems and turn recurring failure patterns into permanent infrastructure fixes.

Skills

Distributed systems

Python

Go

C++

Linux internals

On-call rotations

Research environment

Tools

PyTorch

Ray

Slurm

Kubernetes

NCCL

Job description

Thinking Machines Lab in San Francisco, CA is hiring a Site Reliability Engineer to own the reliability and performance of post-training and RL training runs. You will work side by side with researchers, diagnose failures in real time, and build tooling to improve automation and resilience across clusters and pipelines.

The role emphasizes production engineering for model training, with on-call rotations and a focus on high-availability infrastructure and observability.