Software Engineer: ML Robotics Systems

Generalist AI; San Mateo, Somerville (CA, MA); On-site; USD 170,000 - 250,000; Full time

Job summary

Generalist AI in San Mateo, CA is seeking an experienced engineer to tackle end-to-end problems that make our AI models work better on robots. You will string together distributed Python services, upgrade data pipelines, train models to validate changes, and test in real-world robotic deployments.

The role requires building robust, scalable software and managing cloud infrastructure to process large data at scale. You will work across video data pipelines and a modern ML stack.

Qualifications

• Experience building large-scale distributed applications.
• Experience processing big data (bonus for video data).
• Strong proficiency in Python and modern ML fundamentals.
• Experience with distributed cloud infrastructure and container orchestration.

Responsibilities

• Design and implement new ideas to improve system robustness, scalability, or speed.
• Overhaul existing systems to handle 10x scale.
• Write business logic to provide data access to robots and customers.

Skills

Distributed systems

Python

ML training & deployment

Data pipelines

Cloud infrastructure

Tools

Kubernetes

Docker

AWS/GCP

Spark

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

You will tackle end to end problems that make our AI models work better on robots. You might add new functionality to our video processing data pipeline, then update our ML data loader, then train some models to validate your change, then test those changes in the real world on a robot. This requires stringing together many distributed python services to accomplish a given data processing, or application processing task. It also requires marshaling large quantities of cloud infrastructure to process this business logic efficiently at scale.

You’ll be responsible for:

• Designing and implementing any new idea that can help make our entire system more robust, scalable, or faster.
• Overhauling existing systems and services to handle the next 10x of scale.
• Writing the business logic that gets our robot the data it needs, or the business logic that gives our customers the right access to our robots.

You might thrive in this role if you:

• Have extensive experience building complex distributed applications or data pipelines at scale.
• Have experience processing petabytes of data (bonus if it’s video data).
• Expertise in python, basic distributed infrastructure skills, and solid modern ML fundamentals.
• Have a solid foundation in modern ML techniques and experienced large scale ML training and production deployments.
• Have experience with distributed cloud infrastructure and a solid understanding of cloud networking, permissions, and container orchestration (Kubernetes).