Senior Machine Learning Engineer, Autonomy

RESPONSIBILITIES

• Design and develop advanced machine learning models specifically tailored for autonomous vehicles utilizing deep learning and large-scale data analysis
• Deploy scalable and efficient ML models on our autonomous vehicle platform
• Integrate modern technologies with rigorous safety standards while maintaining cost efficiency
• Significantly contribute to development of needed components for end-to-end ML training and deployment, from data strategy to optimization and validation
• Be a champion of the scientific method and critical thinking in inventing state-of-the-art deep learning solutions
• Work in a high-velocity environment and employ agile development practices
• Exhibit a "Giver" mindset, proactively asking, “What can I do for you?” to facilitate production development processes while maintaining a "get things done" mentality
• Collaborate closely with teams such as Perception, Motion Planning, Simulation, Infrastructure, and Tooling to drive unified solutions
• Work in a hybrid workspace, with the requirement to be present in our Nihonbashi (Japan), Palo Alto (California), or Ann Arbor (Michigan) offices three days per week

MINIMUM QUALIFICATIONS

• MS or PhD in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience
• 3+ years of  experience with Python, any major deep learning framework, and software engineering best practices
• Comfortable in writing C++ code to help integrate with our autonomous vehicle platform
• 2+ years of experience with deep learning approaches such as supervised/unsupervised learning, transfer learning, multi-task learning, and/or deep reinforcement learning
• 2+ years of experience covering machine learning workflows, data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization
• Strong communication skills with the ability to communicate concepts clearly and precisely

NICE TO HAVES

• Published research at top-tier conferences (NeurIPs, CVPR and similar)
• Proven track record of deploying ML models at scale in self-driving or related fields
• Familiarity with production-level coding in time-limited task schedules
• Experience with computer vision (e.g.multi-view geometry, camera calibration, depth estimation, neural radiance fields, gaussian splatting, simultaneous localization and mapping) 
• Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control) 
• Experience with temporal data and/or sequential modeling
• Experience in self-driving challenges (Perception, Prediction, Mapping, Localization, Planning, Simulation)