GPU Reliability Engineer — AI Compute (Unlimited PTO)

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

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines is hiring an engineer to ensure the reliability of our GPU supercomputing fleet, owning the seam between hardware, firmware, and OS. You will diagnose hardware issues, coordinate with vendors, and drive improvements across large GPU clusters to enable scalable research.

The role is evergreen and open on an ongoing basis; apply if you meet core qualifications and reapply as opportunities arise. Strong collaboration with cross‑functional teams is essential.

Qualifications

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (Python or Rust).
• Experience operating large‑scale clusters and container orchestration systems (Kubernetes or Slurm).
• Comfortable operating across the stack and owning projects end‑to‑end.
• Thrive in a highly collaborative environment with cross‑functional partners.
• Bias for action with initiative to work across stacks and teams.

Responsibilities

• Investigate, reproduce, and remediate issues across large GPU clusters.
• Own the drivers, kernel surface, and diagnostics spanning hardware, firmware, and OS.
• Automate monitoring of fleet reliability and analyze error rates to validate fixes.
• Drive the firmware lifecycle: tracking, qualification, rollout, and regression analysis.
• Engage vendors directly to obtain real fixes and manage RMA flows.
• Monitor and improve GPU hardware health signals for reliability gains.
• Write postmortems and vendor cases to move issues forward.

Skills

Python

Rust

Kubernetes

Slurm

Linux

Backward compatibility

Education

Bachelor’s degree or equivalent experience in CS/engineering

Tools

Kubernetes

Slurm

Job description

Thinking Machines is hiring an engineer to ensure the reliability of our GPU supercomputing fleet, owning the seam between hardware, firmware, and OS. You will diagnose hardware issues, coordinate with vendors, and drive improvements across large GPU clusters to enable scalable research.

The role is evergreen and open on an ongoing basis; apply if you meet core qualifications and reapply as opportunities arise. Strong collaboration with cross‑functional teams is essential.