Software Engineer: ML Optimization

Generalist AI, Inc.; San Francisco (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Generalist AI, Inc. in San Francisco is seeking an ML optimization engineer to own optimizations on training and on-robot inference stacks. The role requires pushing step-function gains across hardware, software, and ML components.

You will explore CUDA kernels, model architectures, and bottlenecks from CPU to NVLink, delivering practical improvements in transformer and diffusion-based systems. Join a team with deep roots in AI and robotics.

Qualifications

• Own optimizations on both training and on-robot inference stacks.
• Implement ML, hardware, and software changes that yield step-function gains.
• Proficient with latest ML techniques for training and inference in transformer and diffusion architectures.

Responsabilités

• Implementing ML, hardware, and software changes that lead to step-function gains.
• Optimizing both the inference and training stacks.
• Investigating CUDA kernels, architectures, and bottlenecks from CPU to interconnects.

Description du poste

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

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:

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.