Software Engineer: ML Optimization

Generalist; Somerville (NJ); On-site; USD 150,000 - 210,000; Full time

Job summary

Generalist in New Jersey is seeking a highly skilled ML systems engineer to push the boundaries of training efficiency and real-time robot inference. You will own optimizations across GPUs, software stacks, and hardware interfaces to unlock step-function performance gains.

You will collaborate with researchers and engineers to prototype, benchmark, and deploy models at scale, focusing on transformer and diffusion architectures while minimizing latency and power usage.

Qualifications

• Proficient in ML training and inference optimizations for transformer and diffusion models.
• Experience optimizing GPU and system level bottlenecks.
• Strong coding in Python; familiarity with CUDA is a plus.

Responsibilities

• Improve both training and on-robot inference stacks.
• Implement ML, hardware, and software changes for step-function gains.
• Tune CUDA kernels and data pipelines to remove bottlenecks.

Skills

ML optimization

Transformer models

Diffusion models

CUDA kernels

Python programming

Tools

PyTorch

NumPy

CUDA

Job description

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.