Senior Applied Scientist

Job description

Meet your team You'll join the ALF (Localization Features) team within TomTom's ADAS & ADS Product Unit, which develops the HD maps and ADAS technology that powers real-time location intelligence for major automotive and tech companies. ALF is responsible for the localization features layer of the HD map and the ML systems that extract them from real-world sensor data at world scale. You'll work shoulder-to-shoulder with applied scientists and software engineers, partner with adjacent teams, and own the quality trajectory of features that ship to customers under hard product targets. What you will do: Work with a team of medior and senior engineers and applied scientists to develop high-quality algorithms and ML software that powers TomTom's HD maps for ADAS. Lead the design, implementation, and integration of algorithms, ML systems, and data pipelines within your area of focus, from problem framing through experimentation to production rollout. Drive measurable improvements in output quality (recall, precision, latency, cost) against hard customer-facing targets. Lead well-scoped projects and own components within the team's processing pipelines, from upstream input data through algorithmic and ML processing to validated outputs published to downstream consumers. Tackle complex technical problems at scale: noisy upstream signals, geospatial geometry, ground truth quality, and large-scale evaluation pipelines. Build iteratively using agile methodologies and rigorous experimentation; document outcomes so the team can build on them. Mentor junior engineers and interns, provide insightful code reviews, and contribute to hiring as an interviewer. What you will need: 4+ years of professional Applied Science, Machine Learning, algorithm development, or related experience. Bachelor's degree (minimum) in Computer Science, Machine Learning, Computer Vision, Geospatial Science, Statistics, or a related quantitative field. Master's or PhD is a plus. Solid fundamentals in algorithm design and analysis: data structures, complexity reasoning, and applied algorithms for geospatial and signal-processing problems. Solid fundamentals in machine learning: model training and evaluation, statistics, and experimental design. Proficiency in Python; experience with at least one ML framework ( PyTorch , TensorFlow, or equivalent) and at least one large-scale data processing framewo…