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Generalist

Software Engineer: ML Optimization

San Francisco Bay Area (San Mateo) or Boston (Somerville)
Salary
$260K - $350K
Level
Mid
Work type
On-site · San Francisco Bay Area

Not stated in the posting: visa.

Open. First seen 3 October 2026.

About the role

Generalist's standard text

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, 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.

What this role asks for

Particular to this role

CUDA · NumPy

Rare in Generalist's other 10 open postings.

Standard for data science / ML roles

Machine Learning · Python

Common in data science / ML postings at any company.

36% of this description is written for the role. The rest is Generalist's standard text, folded away below.

Read against Generalist's other open postings on this site.

The team: Technical Staff HIRES STEADILY

This role is one of 10 open in Technical Staff. 1 was posted in the last 30 days and the oldest in February: a team that hires all year rather than one being built.

WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS

Embedded Systems (3 of 10), Python (3 of 10), AWS (2 of 10), C++ (2 of 10), Kubernetes (2 of 10), Machine Learning (2 of 10), React (2 of 10)

OTHER ROLES ON THIS TEAM

From the team and dates on Generalist's own job board.

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