Machine Learning Applied Scientist - ADAS Online

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.

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.

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.

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).

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 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.