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 product targets.

• Work with a team of senior engineers and applied scientists to develop high-quality algorithms and ML software that powers TomTom's HD maps for ADAS.
• Contribute to 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.
• Contribute to measurable improvements in output quality (recall, precision, latency, cost) against hard customer-facing targets.
• Own well-scoped 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.
• Support junior engineers and interns, and contribute to hiring as an interviewer.

• 3+ 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 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 framework (Spark, Databricks, or equivalent).
• Experience taking algorithms, ML models, or data pipelines into production, not just running experiments offline.
• Breadth across applied science: comfortable working across algorithms, modeling, evaluation, ground truth quality, and methodology. 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 (in depth experience is not needed): 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).
• Comfortable with written and verbal communication in English.
• Curiosity and desire to learn, and to expand your skill set across the ML stack.
• Ability to solve a complex problem on your own by leveraging experience, peers, and other resources.