{"id":587688,"url":"https://alion.io/job/tomtom-machine-learning-staff-engineer-adas-online","title":"Machine Learning Staff Engineer – ADAS Online","company":{"id":54490,"name":"TomTom","domain":"tomtom.com","url":"https://alion.io/company/tomtom","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":77,"open_postings":7,"ghost_share":0,"stale_share":0.714,"repost_share":0,"time_to_fill_p50_days":80,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Berlin, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":99000,"max_usd":216000,"period":"year","method":"role_seniority_country_cell","sample_n":8},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Gaussian Splatting","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Perception","optional":false},{"name":"Physical AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Rest-Assured","optional":false},{"name":"Transformers","optional":false},{"name":"Unity","optional":false},{"name":"Vision Transformer","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-09-04T20:25:19Z","employer_posted_date":null,"last_verified_at":"2026-09-04T20:25:19Z","board_verified":false,"closed_at":null,"days_open":56,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":56},"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 ML Staff Engineer, you will drive the algorithmic direction of our present and future in-vehicle spatial awareness stack while remaining deeply hands-on in model design, experimentation, and performance improvement. You will contribute as a senior technical authority and mentor within a small, high-velocity team.\nThis 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. This is a high-ownership technical role in a fast-moving, data-driven AI environment.\nDefine and drive the technical direction for physical AI algorithms\nDefine and execute on a technical roadmap towards state-of-the-art reinforcement learning using physical AI world models.\nDesign, implement, and improve ML / vision transformer models for 3D awareness and planning. These include Gaussian Splatting (3DGS), Diffusion, object detection, multi-object tracking, semantic segmentation, and occupancy modeling\nArchitect multi-modal fusion approaches (camera, LiDAR, RADAR) to build 3D environments\nPer the roadmap, identify where larger end-to-end models should replace more traditional approaches\nApply advanced ML techniques (Transformers, representation learning, large-scale models) to improve perception performance\nLead structured experimentation and benchmarking to deliver measurable gains in accuracy and robustness\nTranslate research ideas into reliable, scalable ML solutions\nProvide technical guidance and mentorship to perception engineers\n7+ years of experience in machine learning, vision transformers, diffusion, or computer vision\nDeep expertise in modern deep learning architectures\nStrong hands-on experience with PyTorch (or equivalent frameworks)\nProven experience building and iterating on large-scale ML models\nStrong mathematical foundations in optimization and probabilistic modeling\nTrack record of delivering measurable improvements in ML system performance\nExperience guiding technical decisions within a small engineering team\nExperience in autonomous systems or robotics perception\nPublications or patents in machine learning or perception\nExperience with 3D data representations (gaussian spatting, point clouds, BEV, voxel grids) and 3D engines like Unity\nFamiliarity with large-scale training or foundation models\nExperience mentoring engineers in advanced ML topics\nWhat we offer\nA competitive compensation package, of course.\nTime 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.\nTime to support life outside of work, with enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.\nWork 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.\nImprove your home office with a setup budget and get extra support with a monthly allowance.\nEnjoy 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.\nTake the holidays you want with a competitive holiday plan, plus an extra day off to celebrate your birthday.\nJoin annual events like our Hackathon and DevDays to bring your ideas to life with talented teammates from around the world.\nBecome a part of our inclusive global culture and have the chance to collaborate with a diverse community - we have over 80 nationalities at TomTom!\nFind out more about our global benefits and enjoy additional local benefits tailored to your location.\nMeet your team\nWe are the ADAS & ADS Product Unit, leading the production of TomTom’s HD maps and ADAS technology.\nIn 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.\nMore 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.\nAt TomTom...\nYou’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.\nWork 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.\nAfter you apply\nOur 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.\nTomTom is an equal opportunity employer\nTomTom 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. 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