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, research.google RT-2 deepmind.google, Gemini Robotics deepmind.google), launched and scaled ChatGPT chatgpt.com and GPT-4 openai.com to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas bostondynamics.com, Spot bostondynamics.com, Stretch bostondynamics.com) and pushed the limits of what they can do (from parkour youtube.com to manipulation bostondynamics.com, and testing robustness youtube.com). 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

Requirements

Must-haves

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.
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlkubernetes

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Vision