Generalist
AI & Machine LearningDirect Ashby career site

Software Engineer: ML Infra

Location
San Francisco Bay Area (San Mateo)
Job type
Full-time
Level
Mid-level
Listed
30 days ago

Skills & highlights for this role

  • GPU cluster management
  • Slurm
  • Kubernetes
  • distributed training
  • ML data loading

Also useful

  • NVIDIA GPU ecosystem
  • high-scale data pipelines

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

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).

Read the original posting

From Generalist's careers page, first listed on Rolev.ai on October 10, 2026. Rolev.ai is not the employer.

How Rolev.ai helps with this application

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Common questions

Where is the Software Engineer: ML Infra job at Generalist located?

This Software Engineer: ML Infra position at Generalist is located in San Francisco Bay Area (San Mateo). Always check Generalist's application page for specific state eligibility or relocation policies.

What qualifications are required for Software Engineer: ML Infra at Generalist?

For the Software Engineer: ML Infra role, Generalist highlights experience with GPU cluster management, Slurm, Kubernetes, distributed training, ML data loading. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.

How do I apply for the Software Engineer: ML Infra position at Generalist?

This listing links directly to Generalist's Ashby application page. You can apply directly on Generalist's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Ashby application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.