Extracted from the posting
TL;DR
Research & Modeling Engineer role to build and train core multimodal/VLA models for Mind's physical AI robots. Covers large-scale training pipelines, data strategy, evaluation, and deployment on real robots in production. Requires Python and experience with LLMs, VLMs, or robotics foundation models.
The full posting
About Mind:
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.
About the team and 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 models sit at the core of this effort, bridging cutting-edge foundation model techniques with real-world robotic execution.
We’re looking for a Research & Modeling Engineer to build and train the core models that power our systems, and ensure they perform reliably on real robots in production environments.
Responsibilities:
- Design and run large-scale training pipelines for multimodal / VLA systems.
- Own the full loop: data → training → evaluation → deployment on real robots.
- Develop scalable infrastructure for data ingestion, training, and iteration.
- Translate model outputs into reliable, high-performance robotic actions.
- Work hands-on with robots to debug, iterate, and improve behavior.
- Define data strategy (quality, scale, diversity) and evaluation frameworks.
- Continuously improve performance across real-world tasks and environments.
Requirements:
- Built and trained large-scale models (LLMs, VLMs, or robotics foundation models).
- Deep understanding of modern ML, training dynamics, and optimization at scale.
- Experience with distributed systems and data pipelines for large-scale training.
- Comfortable operating end-to-end: from data → model → real-world deployment (including robots).
- Domain strength in at least one: robotics, VLA systems, or training LLMs/VLMs from scratch.
- Strong Python proficiency.
Nice to Have:
- Experience with dexterous manipulation or complex robotic tasks.
- Experience deploying models in real-world, production environments.
