Job description

ABOUT THINKING MACHINES 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. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We're hiring a Site Reliability Engineer (SRE) to keep our post-training and reinforcement learning (RL) systems fast, reliable, and easy for researchers to iterate on. Think of this as a production engineering or site reliability role built around model training: you'll own the health of the training runs, clusters, and pipelines that power post-training and RL at Thinking Machines. You'll work side by side with research teams during active model runs — debugging failures in real time, hardening infrastructure against the next class of problem, and building the tooling and automation that let researchers spend their time on the science instead of babysitting jobs. This role has real ownership: you'll be the person a research team calls when a run stalls at 2am, and the person who makes sure it doesn't happen again. WHAT YOU’LL DO - 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 & QUALIFICATIONS MINIMUM 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 PREFERRED QUALIFICATIONS - Experience operating GPU or TPU training clusters at scale - Familiarity with post-training and RL techniques (e.g., RLHF, PPO, DPO) and the infrastructure challenges specific to them, such as reward model serving, rollout generation, and mixed training/inference workloads - Experience with distributed training frameworks (e.g., PyTorch, Ray) and job schedulers (e.g., Slurm, Kubernetes) - Experience with high-performance networking (e.g., InfiniBand, RDMA, NCCL) and its role in distributed training performance - Experience building observability tooling purpose-built for ML training, not just general infrastructure - A track record of thriving in fast-changing, research-driven environments where priorities shift with the science LOGISTICS - Location: This role is based in San Francisco, CA. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000- $350,000 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.

Requirements

Must-haves

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
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
Location: This role is based in San Francisco, CA.
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.

Nice-to-haves

Experience operating GPU or TPU training clusters at scale
Familiarity with post-training and RL techniques (e.g., RLHF, PPO, DPO) and the infrastructure challenges specific to them, such as reward model serving, rollout generation, and mixed training/inference workloads
Experience with distributed training frameworks (e.g., PyTorch, Ray) and job schedulers (e.g., Slurm, Kubernetes)
Experience with high-performance networking (e.g., InfiniBand, RDMA, NCCL) and its role in distributed training performance
Experience building observability tooling purpose-built for ML training, not just general infrastructure
A track record of thriving in fast-changing, research-driven environments where priorities shift with the science

Application prep

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Keywords to mirror

aipythongokubernetesml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Pto

Parental Leave