Network Engineer, Supercomputing

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

Benefits offered by this job

Health, dental, and vision insurance

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines Lab is seeking a Network Engineer in San Francisco to manage and improve our GPU network fabric. The role requires in-depth knowledge of large-scale deployments and the ability to debug complex network issues. A collaborative environment is emphasized, where initiative and effective communication with cloud providers are key.

The position offers a competitive salary ranging from $350,000 to $475,000 per year, depending on skills and experience. Benefits include unlimited PTO and health coverage, alongside visa sponsorship.

Qualifications

• Degree or equivalent in computer science, engineering, or a related field.
• Experience with RDMA/RoCE and debugging networking issues.
• Ability to work across multiple cloud providers.

Responsibilities

• Manage the reliability of GPU network fabric across deployments.
• Debug network issues and automate tooling for efficient debugging.
• Collaborate with cloud providers to resolve connectivity issues.

Skills

Backend language proficiency (Python or Rust)

Experience with large-scale clusters

Comfortable across the tech stack

Collaboration skills

Initiative and action bias

Education

Bachelor’s degree in computer science or related field

Tools

Linux debugging tools

Kubernetes or Slurm

CUDA/NCCL

Job description

Thinking Machines Lab’s mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.

We are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open‑weights models like Mistral, as well as popular open source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.

About the Role

We're looking for a network engineer to own the lowest layers of the network stack that our large‑scale training and inference depend on. A single degraded link or flapping NIC can quietly slow a long training run or take it down outright; you'll be responsible for interconnect reliability at scale, across large GPU fabrics — both the RDMA/RoCE fabric between nodes and the NVLink/NVSwitch domains within them.

This is a hands‑on, cross‑stack role. You'll debug production collectives down to the NIC, build instrumentation and tooling that makes the next debugging session dramatically faster, and serve as the technical point of contact who drives issues to resolution with our cloud providers' networking teams. Your goal is for our researchers to trust the fleet without worrying about the fabric underneath.

What You’ll Do

• Reason about and validate GPU network fabric design across our deployments.
• Debug RDMA / RoCEv2 across different NIC vendors. Diagnose collective failures of production NCCL, PFC/ECN tuning, and congestion control behavior.
• Own NVLink / NVSwitch interconnect — including fabric manager and IMEX health, link and lane errors, and how the GPU fabric interacts with collectives.
• Build host‑level network instrumentation and use Linux tooling to build dashboards and alerts, not just the bug report.
• Navigate cross‑cloud fabric quirks across providers and triage across the NIC, driver, kernel, switch, and workload boundaries.
• Drive escalations with cloud‑provider networking teams, owning issues end‑to‑end until they’re resolved.

Skills and Qualifications

• Bachelor’s degree or equivalent experience in computer science, engineering, or a related field.
• Proficiency in at least one backend language (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 cross‑functional partners and subject‑matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and teams where you spot an opportunity to ship.

Preferred Qualifications

• Fluency with host‑level debugging tools on Linux.
• Strong communication skills, internally and with cloud providers.
• Familiarity with cloud network primitives across at least two cloud providers.
• Hands‑on experience with NVLink / NVSwitch, fabric manager, and IMEX.
• Statistical rigor in reliability reasoning — comfort reasoning about failure and error rates, distributions, and base rates, and the judgement to separate signal from noise when characterizing a large fabric.
• A track record of writing tooling that made the next debugging session meaningfully faster.
• Familiarity with CUDA/NCCL and performance profiling for distributed training and inference.
• Understanding of deep learning frameworks and their underlying system architectures.

Logistics

• Location: San Francisco, California.
• Compensation: $350,000–$475,000 USD per year (depending on background, skills, and experience).
• Visa sponsorship: We sponsor visas and will work through the process together.
• Benefits: Health, dental, and vision; unlimited PTO; paid parental leave; relocation support as needed.

As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab will consider qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.