Senior Machine Learning Engineer - Autonomy

Senior Machine Learning Engineer – Autonomy

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.

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.

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).