GPU Infrastructure Engineer — Scalable AI Training

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. is seeking an engineer to design, build, and operate the GPU supercomputing environment that powers large-scale training and inference. You will deliver high-performant, reliable, and cost-efficient compute so researchers can move fast at scale.

This evergreen role is open on an ongoing basis to express interest. You may apply even if your experience aligns with future opportunities; we review applications continuously and reach out as roles open.

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).
• Comfort operating across the stack and owning projects end-to-end.

Responsibilities

• Operate and automate large GPU clusters including provisioning, imaging, and capacity planning.
• Write software that abstracts cluster management and presents a unified interface for training and inference.
• Extend scheduling/orchestration for topology-aware placement, preemption, quotas, and fair-share multi-tenancy.
• Monitor and improve operational metrics of speed, reliability, and error recovery.
• Build reliable storage and artifact paths for datasets, checkpoints, and logs with clear retention and lineage.
• Partner with researchers to unblock scale runs and advise on parallelism and performance trade-offs.

Skills

Python

Rust

Kubernetes

Slurm

Education

Bachelor’s degree or equivalent experience in CS/Engineering

Tools

CUDA/NCCL

Performance profiling

Linux

Job description

Thinking Machines Lab Inc. is seeking an engineer to design, build, and operate the GPU supercomputing environment that powers large-scale training and inference. You will deliver high-performant, reliable, and cost-efficient compute so researchers can move fast at scale.

This evergreen role is open on an ongoing basis to express interest. You may apply even if your experience aligns with future opportunities; we review applications continuously and reach out as roles open.