Senior Applied Scientist Engineering Maps ADAS & ADS(Maps

Meet your team

You'll join the ALF (Localization Features) team within Tom Tom'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.

• Work with a team of medior and senior engineers and applied scientists to develop high-quality algorithms and ML software that powers Tom Tom'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.

• 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, Tensor Flow, or equivalent) and at least one large-scale data processing framework (Spark, Databricks, or equivalent).
• Experience taking algorithms, ML models, or data pipelines into production, not just running experiments offline, and leading well-scoped projects or components to delivery with minimal guidance.
• Experience mentoring junior colleagues and providing insightful code reviews.
• Breadth across applied science: comfortable working across algorithms, modeling, evaluation, ground truth quality, and methodology, with depth in some of them. You should be able to pick up a new sub-domain quickly rather than only operating in a single area of expertise.
• Working familiarity with several of the following (no need to be deep in all): algorithm design for geospatial and geometric problems (polygon geometry, map-matching, spatial indexing), classical ML and clustering on noisy sensor data, computer vision (detection, segmentation), data and ML pipelines at scale (training pipelines, MLOps, dataset generation).
• Proficient in written and verbal communication in English.
• Curiosity and desire to learn, and to expand your skill set across the ML stack.
• Ability to solve complex problems on your own, taking a new perspective on existing solutions and leveraging your experience, peers, and other resources.

A competitive compensation package, of course.

Time and resources to grow and develop, including a personal development budget and paid leave for learning days, as well as paid access to e-learning resources such as O’Reilly and Linked In Learning.

Time to support life outside of work, with enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.

Work flexibility, where Tom Tom’ers, in agreement with their manager and team, use both the office and home to focus, collaborate, learn and…