Meet your teamYou'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 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.

What We OfferA 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 LinkedIn 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 TomTom’ers, in agreement with their manager and team, use both the office and home to focus, collaborate, learn and socialize. It’s all about getting the best out of both worlds – we ask TomTom’ers to come to the office two days a week, and the remaining three are free to be worked in either location.

Improve your home office with a setup budget and get extra support with a monthly allowance.

Enjoy options to work from your home country and abroad for a set number of days each year, to visit family and friends, or to simply explore the world we’re mapping.

Take the holidays you want with a competitive holiday plan, plus an extra day off to celebrate your birthday.

Join annual events like our Hackathon and DevDays to bring your ideas to life with talented teammates from around the world.

Become a part of our inclusive global culture and have the chance to collaborate with a diverse community – we have over 80 nationalities at TomTom!

Find out more about our global benefits and enjoy additional local benefits tailored to your location.

Meet your teamWe are the ADAS & ADS Product Unit, leading the production of TomTom’s HD maps and ADAS technology.

In a diverse team of applied scientists, engineers, data scientists, and more, equipped with a broad array of expertise, you’ll collaborate on groundbreaking location-based technologies and applications.

More specifically, you’ll be at the forefront of the creation of advanced HD maps. You’ll also help update these in real-time, ensuring our maps are pushing the world forward instantly. These maps will then go on to empower the largest car manufacturers, transportation giants, and major tech companies around the world.

At TomTom...You’ll help people find their way in the world. In 2004, TomTom revolutionized how the world moves with the introduction of the first portable navigation device. Now, we intend to do it again by engineering the first-ever real-time map, the smartest and most useful map on the planet.

Work with a team of 3,300+ unique, curious and passionate problem-solvers. Together, we’ll open up a world of possibilities for car manufacturers, enterprises and developers to help people understand and get closer to the world around them.

After you applyOur recruitment team will work hard to give you a meaningful experience throughout your journey with us, no matter the outcome. Your application will be screened closely and you can rest assured that all follow-up actions will be thorough, from assessments and interviews all the way through onboarding. To find out more about our application process, check out our hiring FAQs .

TomTom is an equal opportunity employerTomTom is where you can find your place in the world. Every day we welcome, nurture and celebrate differences. Why? Because your uniqueness is what makes you, you. No matter your culture or background, you’ll find your impact at TomTom. Research also shows that sometimes women and underrepresented communities can be hesitant to apply for positions unless they believe they meet 100% of the criteria. If you can relate to this, please know that we’d love to hear from you.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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 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.