Machine Learning Engineer, Maps & Navigation, Robotaxi

As a member of the Vehicle Controls and Navigation Algorithms team, you will work on developing accurate maps of the world’s road network, used to power Tesla’s navigation engine which is leveraged by all existing customer cars and future Robotaxis. You will experiment with new algorithmic approaches to build maps, improve their quality, and implement these changes in production systems. Ultimately, you will positively impact customer experience by championing improvements across our map building stack in a data-driven, first-principled way that leverages all the data collected from our fleet of millions of vehicles across the world. This role is highly impactful, critical to the experience of millions of Tesla customers on the road today, and future autonomous Robotaxi fleets.

Responsibilities

Leverage Tesla’s unique data sources and your skills to measure map quality and surfaces issues across elements like lane graphs, connectivity, attributes, points of interest, etc. Automate these workflows, improve their precision, and identify other process gaps, ultimately speeding up pace of map improvements Design, prototype, and deploy state-of-the-art machine learning and data-driven algorithms to efficiently up-level our map building process Use your insights to help prioritize improvements across the map building stack Overall, own and champion map quality – a critical part of our Robotaxi and Full Self Driving experience

Requirements

Strong fundamentals of geo-spatial and topological analytics Understanding of approaches to measure, improve, and validate map and lane graph quality Comfort working with large production scale datasets Strong proficiency in either Python, Go, C++ and libraries for geo-spatial, and graph analysis Experience with GIS and map building/map quality problems at scale preferred Experience with using ML approaches to map problems is a plus Ability to collaborate cross-functionally with software developers to execute on open-ended projects in small, fast-moving teams Ability to clearly communicate actionable analytics and insights to cross functional teams Degree in Statistics, Operations Research, Computer Science, Data Science, or equivalent in experience and evidence of exceptional ability