Robotics Foundation Model Engineer
Tokyo
Automation – Automation - Foundation Model Team /
Full-time /Part-time /Contract (English) /
Hybrid
Position Summary
As the Robotics Foundation Model Engineer, you will design, train, and deploy large-scale multimodal models that integrate vision, language, and action components for real-world robotic applications. Leveraging data from our teleoperation systems, you will create generalizable policies for our robots to perform complex tasks autonomously and reliably—beyond lab-scale or proof-of-concept demos. You will guide the end-to-end pipeline, from data processing and model design to on-robot deployment and performance optimization.
Key Responsibilities
- Develop and refine Vision-Language-Action Models training architectures (transformers, diffusion model and flow matching).
- Real-Time Inference to ensure low-latency and reliable control signals.
- Continuous Field Optimization with hardware and refine model hyperparameters, and optimize inference for new or unexpected scenarios.
- Catch up on cutting-edge research in multimodal deep learning & apply it to robot at the real market issues.
- Performance Evaluation & Safety Checks to validate VLA models (safety, accuracy, and autonomy metrics) in real convenience store environments.
Qualifications
- Technical Skills:
- Deep Learning Expertise: Demonstrated track record building and training learning based controller models (e.g., imitation Learning, Reinforcement Learning). No robotics control experienced candidates (e.g., just Perception ML Enginner) are not qualified.
- Robotics Integration: Experience deploying AI/ML solutions onto physical robots with real-time constraints; proficiency using robotics middleware (e.g., ROS1/2) and embedded edge hardware (e.g., Jetson).
- Data Engineering for ML: Proficiency in constructing data-processing pipelines (Python, C++, or similar. Training using high-performance GPU) for large, complex datasets (images, video, text, sensor logs).
- Control & Actuation: Solid understanding of control theory and how high-level AI actions map to low-level motors, actuators, and physical robot systems.
- Professional Experience:
- Robust Deployment Track Record: Proven success in taking advanced ML/AI or robotics projects from initial research to stable, real-world operation (beyond Research & PoC).
- Industry & Research Contributions: Strong portfolio or publication record in AI or robotics; comfortable presenting at conferences is a plus.
- Soft Skills & Culture Fit:
- Comfortable in a performance-driven environment (high rewards for results, potential demotion for underperformance).
- Communication skills in English; Japanese proficiency is a plus.
Applications consisting solely of a standard resume without addressing these points will not proceed in our selection process. We look forward to reviewing your concrete evidence of expertise in building and deploying advanced robotics foundation models.
