Software Engineer: ML Optimization

Generalist AI; San Mateo, Somerville (CA, MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Generalist AI is seeking a highly capable ML optimization engineer to advance both training and on-robot inference stacks. You will push step-function gains across GPUs and software paths, shaping how models operate in real-world robotics tasks.

You will explore transformer and diffusion model optimizations, from CUDA kernels to Python-level improvements, aiming for substantial throughput with minimized latency in production pipelines.

Qualifications

• Experience with training and inference optimization for transformer and diffusion models.
• Proficiency with CUDA kernels and GPU optimization.
• Strong knowledge of Python, PyTorch, and NumPy.
• Experience optimizing both hardware and software paths.

Responsibilities

• Own optimizations on training and on-robot inference stacks.
• Implement ML, hardware, and software changes that enable step-function gains.
• Optimize both inference and training stacks.

Skills

ML optimization

CUDA kernels

Python performance

Transformer architectures

Diffusion models

NVLink

Tools

CUDA

PyTorch

NumPy

Python

Job description

About GeneralistAt 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:

• 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.