Your team
You will join the ALF (Localization Features) team within TomTom's ADAS & ADS Product Unit, which develops HD maps and ADAS technology for real-time location intelligence for major automotive and technology companies. ALF is responsible for the localization features layer of the HD map and the ML systems that extract it from real-world sensor data at global scale.
You work closely with applied scientists and software engineers, collaborate with adjacent teams, and are responsible for the quality trajectory of features shipped to customers under strict product targets.
What you will do
- Work with a team of mid-level and senior engineers and applied scientists to develop high-quality algorithms and ML software that power TomTom's HD maps for ADAS.
- Lead the design, implementation and integration of algorithms, ML systems and data pipelines within your focus area, from problem definition through experimentation to production deployment.
- Drive measurable improvements in output quality (recall, precision, latency, cost) against tight customer-focused targets.
- Lead well-scoped projects and take responsibility for 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 with agile methodologies and rigorous experimentation; document results so the team can build on them.
- Mentor junior engineers and interns, provide thoughtful code reviews and contribute to hiring as an interviewer.
What you need
- 4+ years of professional experience in Applied Science, Machine Learning, algorithm development or a related field.
- 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 and at least one large-scale data processing framework.
- Experience with h