Software Engineer: ML Infra

Generalist AI; Boston (MA); On-site; USD 140,000 - 210,000; Full time

Job summary

Generalist AI in Boston is building large robot foundation models, requiring massive GPU hardware and distributed training systems. You will own and optimize both training and inference infrastructure, including on‑prem GPUs connected to robots with strict real‑time latency budgets.

You will manage GPU compute fleets, ensure researchers can access GPUs easily, and optimize data loading and storage across distributed environments.

Qualifications

• Experience managing large GPU fleets for distributed training or inference.
• Deep experience with Slurm or Kubernetes for ML workloads.
• Experience building high-scale ML data loaders and pipelines.
• Strong understanding of ML hardware, storage and networking stacks.
• Experience working with NVIDIA GPU ecosystem and tooling.

Responsibilities

• Own our GPU compute fleets and ensure they are reliable and well‑documented.
• Make GPUs easy for researchers to use and maximize utilization.
• Optimize ML data loading, transport, and storage in highly distributed environments.
• Orchestrate robot inference fleets to meet real‑time latency requirements.

Skills

GPU fleet management

Slurm/Kubernetes

ML data loaders

Hardware/storage/networking know‑how

NVIDIA ecosystem

Tools

Slurm

Kubernetes

Job description

About Generalist

At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.

We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.

The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).

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.

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