Software Engineer, Supercomputing

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

Benefits offered by this job

Health/dental/vision

Unlimited PTO

Parental leave

Relocation support

Job summary

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

You will automate clusters, extend scheduling with Kubernetes or Slurm, monitor metrics, and collaborate with researchers to optimize parallelism and performance.

Qualifications

• Bachelor's degree in computer science, engineering, or similar.
• Proficiency in Python or Rust.
• Experience operating large-scale clusters and container orchestration (Kubernetes or Slurm).
• Ability to own projects end-to-end and work across stacks.

Responsibilities

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

Skills

Python

Rust

Kubernetes

Slurm

Cluster ops

Cross-functional collaboration

Education

Bachelor's degree in CS

Tools

CUDA

NCCL

PyTorch

TensorFlow

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 looking for 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 our users and researchers can move fast at scale.

What You’ll Do

• 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 (Kubernetes, Slurm, or similar) 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 and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.

• Proficiency in at least one backend language (we use Python or Rust).

• Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).

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

• Thrive in a highly collaborative environment involving many, different cross‑functional partners and subject matter experts.

• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Strong systems background: Linux, networking, and infrastructure‑as‑code.

• Familiarity with CUDA/NCCL and performance profiling for distributed training/inference.

• Prior work supporting large‑scale model training or inference environments.

• Understanding of deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and their underlying system architectures.

• Track record of working in fast‑paced environments balancing care with urgency.

Logistics

• Location: This role is based in San Francisco, California.

• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 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.