GPU Supercomputing Reliability Engineer — Unlimited PTO

Mosaic.tech; 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 operating system. You will diagnose hardware anomalies, track root causes to the hardware, and coordinate fixes with vendors so researchers can run at scale.

Based in San Francisco, this full-time role requires owning drivers, kernel surfaces, and diagnostics, plus automating fleet monitoring and reliability improvements across multi-disciplinary

Qualifications

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in Python or Rust and ability to own projects end-to-end.
• Experience operating large-scale clusters and container orchestration (Kubernetes/Slurm).
• Comfortable owning end-to-end projects and working across different stacks and teams.
• Fluency in debugging hardware issues and collaborating with hardware vendors.

Responsibilities

• Investigate, reproduce, and remediate issues across large GPU clusters.
• Own the drivers, kernel surface, and diagnostics that span hardware, firmware, and OS.
• Automate the monitoring of fleet reliability and analyze error rates to validate fixes.
• Drive the firmware lifecycle: tracking, qualification, staged rollout, and regression analysis.
• Engage vendors directly to get real fixes; manage RMA flows when hardware needs replacement.
• Monitor and improve GPU hardware health signals and turn them into actionable improvements.
• Write postmortems and vendor cases to move issues forward.

Skills

Python

Rust

Kubernetes

Slurm

Linux systems

Hardware debugging

Education

Bachelor’s degree or equivalent experience

Tools

Kubernetes

Slurm

BMC / iDRAC / IPMI / Redfish

Job description

Thinking Machines is hiring an engineer to ensure the reliability of our GPU supercomputing fleet, owning the seam between hardware, firmware, and operating system. You will diagnose hardware anomalies, track root causes to the hardware, and coordinate fixes with vendors so researchers can run at scale.

Based in San Francisco, this full-time role requires owning drivers, kernel surfaces, and diagnostics, plus automating fleet monitoring and reliability improvements across multi-disciplinary