Research + Modeling

Mind Robotics Inc.; Palo Alto (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Mind Robotics Inc. is seeking a Research & Modeling Engineer to develop generalized physical AI systems. You'll design training pipelines and ensure models perform reliably on robots in production settings.

The ideal candidate has deep knowledge of machine learning and Python, with experience in large-scale model training and end-to-end deployment. Join us in pushing the boundaries of robotics technology!

Qualifications

• 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.

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.

Skills

Large-scale model training

Deep understanding of ML

Distributed systems experience

End-to-end deployment

Domain strength in robotics

Strong Python skills

Job description

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

Qualifications

• 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 (incl. robots)

• Domain strength in at least one: robotics, VLA systems, or training LLMs/VLMs from scratch

• Strong Python skills

Nice to Have

• Experience with dexterous manipulation or complex robotic tasks

• Experience deploying models in real-world, production environments