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About the Role
As an ML Engineer within the Application Engineering team, you will lead critical initiatives that push the frontier of model-based autonomous driving, focusing on core driving performance and feature-level intelligence like personalization, comfort, and collaboration. You will design and deliver ML-driven behaviors that scale from assisted to autonomous driving, spanning model architecture, data pipelines, evaluation frameworks, and real-world deployment. Collaboration with AI Platform, Simulation, Robot SW, and Model Release teams is key to building performant, adaptable, and production-ready systems.
Responsibilities
- Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
- Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
- Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
- Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
- Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.
- Collaborate cross-functionally across various teams to ensure integration and iteration velocity.
- Mentor senior engineers and shape the long-term technical direction across Autonomy.
Requirements
- Extensive and proven track record of shipping deep learning systems to production.
- Expert in deep learning (esp. sequential models, control, planning, or perception).
- Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
- Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.
- Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.
- Prior work in autonomous driving, imitation learning, or trajectory prediction.
- Familiarity with personalization, human behavior modelling, or driver intent inference.
- Experience integrating ML systems into production hardware or multi-agent simulation.
Skills
- Deep learning
- Sequential models
- Control
- Planning
- Perception
- Python
- C++
- CUDA
- PyTorch
- Software engineering practices
- Real-time systems
- Robotics
- Simulation
- Vehicle-in-the-loop
- Autonomous driving
- Imitation learning
- Trajectory prediction
- Personalization
- Human behavior modelling
- Driver intent inference
- ML systems integration
- Production hardware integration
- Multi-agent simulation
Location
- Japan
Work Type
- Full-time
- Hybrid
Experience Level
- Senior