Research Scientist: Pretraining

Generalist AI Inc.; Boston (MA); Sur place; USD 150 000 - 230 000; Plein temps

Résumé du poste

Generalist AI, Inc. seeks a Senior/Staff ML Engineer to build the base intelligence layer for robotics. You will train large-scale robot foundation models from vast multimodal data, spanning video, proprioception, action traces, language, and more.

You will design and run core training efforts that enable new capabilities across embodiments and environments. If you have worked on pretraining large-scale ML systems or generative foundation models (multimodal, language, audio), this role may be

Qualifications

• Experience with large-scale ML systems and robotics data.
• Proven ability to run multi-node, multi-GPU training.
• Understanding of scaling laws and model reliability.

Responsabilités

• Designing and executing large-scale pretraining runs for robot foundation models (transformer- and diffusion-based architectures)
• Defining model architectures, objectives, and training curricula across multimodal robotic data (vision, action, state, language)
• Developing scalable data mixtures and sampling strategies across petabyte-scale datasets
• Guiding data collection operations toward new directions and sourcing new datasets
• Running ablations to understand scaling laws, data quality effects, and architecture tradeoffs
• Collaborating with ML Infra and Systems to optimize cluster utilization and reliability
• Turning raw robotic interaction data into generalizable model capabilities
• You will live and breathe all forms of robot data.

Connaissances

PyTorch fundamentals

Distributed training

Scaling laws

Data quality assessment

Empirical rigor

Robot foundation models

Debugging at stack level

Outils

PyTorch

Description du poste

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

You will build the base intelligence layer for robotics. We train large-scale robot foundation models from massive multimodal datasets spanning video, proprioception, action traces, language, and more. You will design and run the core large-scale training efforts that give our models fundamentally new general capabilities across embodiments, tasks, and environments. You will “live and breathe” all forms of robot data.

If you have worked on pretraining large-scale ML systems or generative foundation models e.g. multimodal, language, audio, etc. (not only robotics), then this may be the role for you.

You’ll be responsible for:

Designing and executing large-scale pretraining runs for robot foundation models (transformer- and diffusion-based architectures)

Defining model architectures, objectives, and training curricula across multimodal robotic data (vision, action, state, language)

Developing scalable data mixtures and sampling strategies across petabyte-scale datasets

Guiding data collection operations towards new directions, as well as sourcing new datasets

Running ablations to understand scaling laws, data quality effects, and architecture tradeoffs

Collaborating closely with ML Infra and Systems to push cluster utilization, throughput, and reliability

Turning raw robotic interaction data into generalizable model capabilities

You might thrive in this role if you:

Have worked on large-scale ML systems with senior/staff (L6+) experience.

Have led or significantly contributed to multi-node, multi-GPU distributed training efforts

Have worked on scaling laws, optimization dynamics, and large-model failure modes

Have strong PyTorch fundamentals and comfort debugging at every layer of the stack

Care about both empirical rigor and raw iteration speed

Are excited about building general-purpose robot intelligence from first principles