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 We internally call this team MBMB (More Big More Better). You will own optimizations on both the training and on-robot inference stacks. We are still in a regime of step-function, not incremental, gains. You’ll be responsible for: - Making GPUs go brrrrr - Implementing ML, hardware, and software changes that lead to step-function gains - Optimizing both the inference and training stacks You might thrive in this role if you: - Are proficient and stay current with the latest ML techniques for training and inference optimizations in transformer and diffusion based architectures - Will chase ML optimizations anywhere: From the CUDA kernels, to ML architecture, to frontend or backend network bottlenecks, CPU bottlenecks, NVLink and comms, to torch, numpy, and Python inefficiencies.

Requirements

Must-haves

We internally call this team MBMB (More Big More Better). You will own optimizations on both the training and on-robot inference stacks. We are still in a regime of step-function, not incremental, gains.
Making GPUs go brrrrr
Implementing ML, hardware, and software changes that lead to step-function gains
Optimizing both the inference and training stacks
You might thrive in this role if you:
Are proficient and stay current with the latest ML techniques for training and inference optimizations in transformer and diffusion based architectures
Will chase ML optimizations anywhere: From the CUDA kernels, to ML architecture, to frontend or backend network bottlenecks, CPU bottlenecks, NVLink and comms, to torch, numpy, and Python inefficiencies.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aigomlpython

Platform: ashby
Effort: quick

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