Software Engineer: ML Infra

Generalist; Somerville, San Mateo (MA, CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Generalist is seeking a talented individual in Somerville, Massachusetts to manage GPU fleets for large-scale robot training. The role involves optimizing data loading systems and orchestrating inference fleets, ensuring high performance under compute constraints.

The ideal candidate has deep experience with Slurm or Kubernetes for machine learning orchestration and a solid understanding of the Nvidia GPU ecosystem. Come join an equal opportunity employer that values diversity!

Qualifications

• Experience managing large fleets of GPUs for distributed training.
• Deep experience with Slurm or Kubernetes for ML workloads.
• Knowledge in building high-scale ML data loaders.

Responsibilities

• Own the GPU compute fleets and ensure maximal utilization.
• Optimize ML data loading transport and storage.
• Orchestrate robot inference fleets.

Skills

GPU resource management

ML workload orchestration (Slurm or Kubernetes)

Data loading optimization

Understanding of ML hardware

Experience in NVidia GPU ecosystem

Job description

About the Role

Generalist trains very large robot foundation models. This requires utilizing very large numbers of the latest generation GPU hardware and infrastructure (currently Nvidia) to run distributed training jobs and researcher experiments. We have extreme requirements on storage and data loading infrastructure that requires maximizing cloud infrastructure and custom solutions.

You will also own inference infrastructure. For our robots this is a fleet of on-prem GPUs attached to robots that have extreme real-time and latency budgets in compute constrained environments.

You’ll be responsible for:

• Owning our GPU compute fleets
• Ensure our GPUs are easy for researchers to use and maximally utilized
• Optimizing and improving ML data loading transport and storage in highly distributed fully utilized environments.
• Orchestration of robot inference fleets

You might thrive in this role if you:

• Have managed large fleets of GPUs doing large-scale, long-term, highly distributed training runs or inference
• Deep experience in Slurm or Kubernetes for ML workload orchestration
• Have build high-scale ML data loaders and preparation systems
• Deeply understand every layer of the ML hardware, storage, and networking stacks
• Have experience in the NVidia GPU ecosystem

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.