GPU Fleet ML Infra Engineer: Distributed Training & Inference

Generalist; Medford Hillside (MA); On-site; USD 180,000 - 260,000; Full time

Job summary

Generalist is building general intelligence for the physical world, deploying large robot foundation models and managing massive GPU infrastructure. You will own our GPU compute fleets, optimize real-time inference on‑prem systems, and ensure researchers can access and utilize hardware efficiently for distributed training and inference workloads.

You’ll bring deep expertise in Slurm or Kubernetes for ML workloads, experience with large‑scale data loaders, and a solid grasp of ML hardware,

Qualifications

• Managed large fleets of GPUs for long‑running distributed training or inference.
• Deep experience with Slurm or Kubernetes for ML workload orchestration.
• Built high‑scale ML data loaders and preparation systems.
• Deep understanding of ML hardware, storage and networking stacks.
• Experience with NVidia GPU ecosystem.

Responsibilities

• Own our GPU compute fleets and ensure resources are efficiently utilized.
• Make GPUs easy for researchers to use and maximally productive.
• Optimize ML data loading, transport and storage in distributed environments.
• Orchestrate robot inference fleets across on‑prem infrastructure.

Skills

GPU fleet management

ML workload orchestration

Distributed training experience

Networking & storage understanding

Tools

Slurm

Kubernetes (ML)

NVidia GPU ecosystem

Job description

Generalist is building general intelligence for the physical world, deploying large robot foundation models and managing massive GPU infrastructure. You will own our GPU compute fleets, optimize real-time inference on‑prem systems, and ensure researchers can access and utilize hardware efficiently for distributed training and inference workloads.

You’ll bring deep expertise in Slurm or Kubernetes for ML workloads, experience with large‑scale data loaders, and a solid grasp of ML hardware,