Research + Modeling
Join Mind Robotics as a Research & Modeling Engineer to build and train core models for real-world robotic systems.
Posted by employer 7 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
null
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In the Research & Modeling role at Mind Robotics, the engineer is responsible for designing and executing large-scale training pipelines for multimodal systems, ensuring that models translate effectively into robotic actions. Key skills include expertise in machine learning, particularly in building and training large-scale models, as well as proficiency in Python and experience with distributed systems. This position is ideal for someone with a strong background in robotics or related fields, who is comfortable working hands-on with robots to refine their performance. Mind Robotics emphasizes a collaborative environment where innovative problem-solving is essential for advancing physical AI
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from Mind Robotics's career page.
- Mind Robotics has 30 other open roles including Tech Lead, Robotics Runtime & Middleware, IT Operations Lead, Software Engineer, Edge & Field Systems.
- 6527 active Python roles on Joblaze right now.
- 799 active Machine Learning roles on Joblaze right now.
- 248 active Robotics roles on Joblaze right now.
- Typical Python salary we track runs $125,000–$190,800 (median $160,000, across 3229 roles). See Python salary trends
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Machine Learning, Python, Robotics, VLA systems.
- What seniority level is this role?
- Mind Robotics targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Research + Modeling role at Mind Robotics.
From the original 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.