Machine Learning Applied Scientist - ADAS Online Engineering Maps ADAS & ADS(Maps) Amsterdam, The Netherlands

TomTom; Amsterdam; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Personal development budget

Learning days

E-learning access (O'Reilly & LinkedIn

Home office setup budget

Work from abroad option

Birthday day off

Job summary

TomTom is building a high-performance ADAS Online team focused on advancing state-of-the-art machine learning and AI algorithms for scene understanding and environmental awareness.

The machine learning applied scientist will contribute to developing models for the in-vehicle spatial awareness stack, handling data, experimentation, and evaluation pipelines in a fast-moving environment.

Qualifications

• 3+ years of experience in machine learning, vision transformers, diffusion, or computer vision.
• Expertise in modern deep learning architectures.
• Hands-on experience with PyTorch (or equivalent frameworks).
• Understanding of what is needed to build and deploy large-scale ML models.
• Strong ownership and bias toward execution.
• Data-driven mindset with rigorous evaluation practices.
• Ability to operate effectively in fast-paced environments.
• Clear communication of technical trade-offs.
• High standards for code quality and reproducibility.
• Comfortable navigating ambiguity in complex technical domains.

Responsibilities

• Develop a solid understanding of the team's physical AI roadmap and contribute ideas that improve algorithms within your area of ownership.
• Implement and improve ML / vision transformer models for 3D awareness.
• Produce instance detections and segmentation masks, instance tracking, and occupancy modelling.
• Contribute to multi-modal fusion pipelines (camera, LiDAR, RADAR), building and validating components against defined requirements.
• Apply Transformer-based and representation learning techniques within established frameworks, building confidence in large-scale model development.
• Run experiments and benchmarks with guidance, producing clear results and contributing to the team's evaluation practices.
• Participate in translating research into production, learning what makes ML solutions reliable and scalable.
• Actively seek feedback from senior engineers and apply it to continuously improve the quality of your work.

Skills

Machine learning

Vision transformers

PyTorch

Model deployment

Data-driven evaluation

Effective communication

Working in fast-paced environments

Code quality

Ambiguity navigation

Job description

We are building a high-performance ADAS Online team focused on advancing state-of-the-art machine learning and AI algorithms for scene understanding and environmental awareness.

As a machine learning applied scientist, you will contribute to the development and improvement of models within the in-vehicle spatial awareness stack. Work hands-on across data, experimentation, and evaluation pipelines, and grow your ability to tackle increasingly complex engineering challenges in a fast-moving team.

This spatial awareness stack will leverage modern transformer and end-to-end architectures to transform vehicle sensor data along with real-time 3D map data from the cloud into a 3D, semantically defined environment identifying all static and dynamic objects.

What You’ll Do

• Develop a solid understanding of the team's physical AI roadmap and contribute ideas that improve algorithms within your area of ownership;
• Implement and improve ML / vision transformer models for 3D awareness;
• Produce instance detections and segmentation masks, instance tracking, and occupancy modelling;
• Contribute to multi-modal fusion pipelines (camera, LiDAR, RADAR), building and validating components against defined requirements;
• Apply Transformer-based and representation learning techniques within established frameworks, building confidence in large-scale model development;
• Run experiments and benchmarks with guidance, producing clear results and contributing to the team's evaluation practices;
• Participate in translating research into production, learning what makes ML solutions reliable and scalable;
• Actively seek feedback from senior engineers and apply it to continuously improve the quality of your work.

What You'll Need

• 3+ years of experience in machine learning, vision transformers, diffusion, or computer vision;
• Expertise in modern deep learning architectures;
• Hands-on experience with PyTorch (or equivalent frameworks);
• Understanding of what is needed to build and deploy large-scale ML models;
• Strong ownership and bias toward execution;
• Data-driven mindset with rigorous evaluation practices;
• Ability to operate effectively in fast-paced environments;
• Clear communication of technical trade-offs;
• High standards for code quality and reproducibility;
• Comfortable navigating ambiguity in complex technical domains.

Nice to Have

• Experience in autonomous systems or robotics perception;
• Publications or patents in machine learning or perception;
• Experience with 3D data representations (meshes, point clouds, BEV, voxel grids).

What we offer

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 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 TomTomers, 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 TomTomers 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 team

We 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.

TomTom is an equal opportunity employer

TomTom 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.