Software Engineer: ML Optimization

Generalist; Somerville, San Mateo (MA, CA); On-site; USD 100,000 - 150,000; Full time

Job summary

Generalist in Somerville, Massachusetts is seeking a motivated individual dedicated to making general-purpose robots a reality. In this role, you will take charge of optimizations for both training and on-robot inference stacks, leading significant advancements in AI techniques.

The ideal candidate thrives in a dynamic, innovative environment and is proficient in the latest machine learning methodologies. We aim for groundbreaking changes that transform how robots can intelligently interact with the physical world.

Qualifications

• Proficient in the latest ML techniques for optimizing robots.
• Chase optimizations across CUDA kernels and hardware bottlenecks.
• Stay current with advancements in AI implementations.

Responsibilities

• Own optimizations on both training and on-robot inference stacks.
• Implement ML and software changes for significant improvements.
• Optimize both the inference and training stacks.

Skills

Proficiency in ML techniques for training and inference optimizations

Experience with CUDA, torch, numpy, and Python

Understanding of transformer and diffusion-based architectures

Job description

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.

About Generalist

At Generalist, we are on a mission to make general-purpose robots a reality. 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.