Generalist
AI & Machine LearningDirect Ashby career site

Software Engineer: ML Optimization

Location
San Francisco Bay Area (San Mateo) · On-site
Job type
Full-time
Level
Senior
Listed
30 days ago

Skills & highlights for this role

  • CUDA
  • PyTorch
  • transformer inference optimization

Also useful

  • diffusion models
  • NVLink
  • distributed training

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 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).

Equal opportunity and legal notices

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.

Read the original posting

From Generalist's careers page, first listed on Rolev.ai on October 10, 2026. Rolev.ai is not the employer.

How Rolev.ai helps with this application

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Common questions

Where is the Software Engineer: ML Optimization job at Generalist located?

This Software Engineer: ML Optimization position at Generalist is located in San Francisco Bay Area (San Mateo) (On-site). Always check Generalist's application page for specific state eligibility or relocation policies.

What qualifications are required for Software Engineer: ML Optimization at Generalist?

For the Software Engineer: ML Optimization role, Generalist highlights experience with CUDA, PyTorch, transformer inference optimization. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.

How do I apply for the Software Engineer: ML Optimization position at Generalist?

This listing links directly to Generalist's Ashby application page. You can apply directly on Generalist's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Ashby application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.

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