Mind Robotics builds intelligent, broadly capable robots for industrial deployment in high-impact environments. Backed by Accel, Kleiner Perkins and a16z.
About the role
At Mind Robotics, we’re building generalized physical AI —robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our ability to iterate quickly on large-scale models depends on world-class ML infrastructure. We’re looking for a Research Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment.
What they're looking for
- Strong experience building infrastructure for large-scale ML training
- Deep understanding of how modern LLM/VLM systems are trained and scaled
- Proven experience setting up and scaling distributed training across hundreds of GPUs
- Strong understanding of parallelization strategies (data, model, pipeline parallelism)
- Strong proficiency in Python programming
- Expert-level proficiency in PyTorch and/or JAX
More about this role
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
At Mind Robotics, we’re building generalized physical AI —robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our ability to iterate quickly on large-scale models depends on world-class ML infrastructure.
We’re looking for a Research Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment.
Design and implement scalable systems for training large ML models.
Enable efficient workflows for data ingestion, training, and iteration.
Develop and optimize distributed training systems across hundreds of GPUs.
Implement strategies for parallelization, sharding, and efficient compute...
Browse similar: AI jobs · AI startup jobs · Startup jobs · San Francisco Bay Area