Site Reliability Engineer: Large-Scale ML Training & RL

Thinking Machines Lab Inc.; San Francisco (CA); Híbrido; USD 300.000 - 350.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Descripción de la vacante

Thinking Machines Lab Inc. is seeking a Site Reliability Engineer to own the health of post-training and RL systems in a fast-paced research environment. You will work with researchers during active model runs, diagnose failures across the stack, and build tooling to automate recovery and improve throughput.

This role emphasizes production ownership, collaboration with researchers, and building robust infrastructure to minimize downtime and expedite scientific progress.

Formación

• 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.

Responsabilidades

• 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

Conocimientos

Python

Go

C++

Linux

Networking

Distributed systems

On-call rotations

Herramientas

PyTorch

Ray

Slurm

Kubernetes

Descripción del empleo

Thinking Machines Lab Inc. is seeking a Site Reliability Engineer to own the health of post-training and RL systems in a fast-paced research environment. You will work with researchers during active model runs, diagnose failures across the stack, and build tooling to automate recovery and improve throughput.

This role emphasizes production ownership, collaboration with researchers, and building robust infrastructure to minimize downtime and expedite scientific progress.