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
- Model Architecture & Implementation:
- Design Vision-Language-Action Models
- Develop and refine network architectures (transformers, multimodal encoders) that integrate vision data, language instructions, and robot control signals to output intelligent action policies.
- Scalable Training Pipelines: Set up robust machine learning pipelines (distributed training, large-batch processing) to handle extensive teleoperation datasets.
- Real-Time Control Integration: Work closely with our robotics control team to ensure model outputs align with real-time actuation requirements, bridging deep learning inference with embedded controllers.
- Real-World Robot Deployment:
- On-Robot Inference & Optimization: Package and deploy trained policies onto embedded compute platforms (NVIDIA Jetson or similar), ensuring low-latency inference and reliable control signals.
- Performance Evaluation & Safety Checks: Establish rigorous evaluation protocols (safety, accuracy, and autonomy metrics) to validate VLA models in real industrial or field environments, not just in simulation.
- Continuous Field Optimization: Work hand-in-hand with hardware teams and site operators to diagnose issues, refine model hyperparameters, and optimize inference for new or unexpected scenarios.
- Continuous Optimization & Innovation:
- Metrics & Model Health: Define key performance indicators (accuracy, success rate, real-time efficiency) for model-driven robot autonomy and continuously track improvements.
- Research & Knowledge Sharing: Stay up-to-date with advancements in multimodal deep learning, large-scale model optimization, and robotic control research; share breakthroughs internally.
Qualifications
- Technical Skills:
- Deep Learning Expertise: Demonstrated track record building and training large-scale multimodal or transformer-based models (e.g., vision-language transformers, reinforcement learning pipelines).
- 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).
- Distributed Systems: Familiarity with distributed training paradigms (PyTorch Distributed or similar) for large-scale model development.
- 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 simulation and PoC).
- Industry & Research Contributions: Strong portfolio or publication record in AI or robotics; comfortable presenting at conferences.
- Soft Skills & Culture Fit:
- Ownership mentality: Takes responsibility for outcomes and problem-solves proactively.
- User-Centric Mindset: Demonstrates the ability to understand how diverse stakeholders (including end users, partners, and internal teams) will interact with the product, envision optimal workflows, communicate these concepts clearly to both technical and non-technical audiences, and translate them into actionable technical requirements.
- Comfortable in a performance-driven environment (high rewards for results, potential demotion for underperformance).
- Communication skills in English; Japanese proficiency is a plus.
Application & Evaluation
- To help us evaluate your candidacy effectively, please provide detailed examples of your past work that demonstrate your ability to deliver robust, AI-driven robotics solutions. Submitting only generic or unrelated experience (e.g., “I have done some machine learning; trust me”) will not suffice. Instead, please explicitly detail:
- Project Portfolios & Videos: Links to notable projects or demonstrations showcasing successful real-world or near-real-time robot deployments.
- Technical Explanations: Summaries of your role in designing data pipelines, training large-scale models, integrating AI with physical robot systems, and managing any real-time constraints.
- Relevant Publications/Patents: If applicable, include research papers or intellectual property that illustrates your expertise in multimodal AI or robotics.
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