Senior Machine Learning Engineer

The Road Features Group, a sub-organization within ADAS & ADS at TomTom, is the algorithmic engine that drives the creation of highly accurate HD maps to facilitate lane level navigation of Autonomous Vehicles. We create digital twins of road networks faster and more accurately than ever before. Utilizing diverse data sources including on-vehicle, aerial and street-side sensors producing semantic 3D data, we ensure comprehensive extraction of road surfaces, drivable lanes, traffic behaviors, and physical structures. Perceptionis pivotal in this effort, extracting paint on roads, from lane dividers to pedestrian crossings and surface signs, as well as vertical features like poles and Traffic Signs. Join us in setting new standards for mapping technology and making road feature extraction smarter and more efficient.   We are searching for a senior / mid-senior Machine Learning Engineer, with a strong foundation in software engineering and computer vision. In this role, you will support the development of perception models by driving their efficient data, training and inference lifecycles.  This includes productionizing models and pipelines that initially run off-line in the cloud and transition on-line in dedicated hardware on TomTom vehicles. These models will transform diverse sensor data into road feature observations that power TomTom maps.  

What you'll do:

• Working directly with a group of Perception Applied Scientists (AS), you will enable highly efficient training and inference of object detection ML models.  You will provide input to the AS group on what frameworks can and cannot be used on-line. 
• Identify, design and implement core components to ensure the transition of models from offline, cloud-based processing to on-vehicle hardware like Nvidia Jetson family of devices. This includes model export (e.g. ONNX, TensorRT)  
• Work with TomTom’s Mobile Mapping group and embedded engineers to ensure smooth execution of the perception model(s) in vehicle.  
• Drive the technical decisions to improve engineering, test, and operational excellence best practices. 
• Develop resilient systems for the processing and delivery of data. 
• Take charge of projects, both within your team and across the organization, to ensure successful outcomes. 
• Support hiring for team vacancies, and investments into team and product health. 
• Be involved in every phase of the project lifecycle, from gathering requirements to designing, coding, testing, and integrating solutions. 

What you'll need:

• Bachelor's degree in Computer Science or a related field, and5 years of experience in modern programming languages such as Python and C++ for embedded systems. 
• Experience with embedded data processing tools and frameworks, particularly related to ML Models.  For example, ONNX, TensorRT, Nivida SDK Manager. 
• Experience with sensor integration in an embedded environment.  Such as the collection of camera images and streaming to the Object detection models. (e.g.DeepStream).
• Ability to quickly learn new languages and technologies, and lead others in doing the same. 
• Comfortable with both written and verbal communication in English. 
• Solid understanding of computer science principles, including object-oriented design, data structures, algorithm design, and complexity analysis. 
• In-depth knowledge of professional software engineering practices across the full software development lifecycle, including coding standards, code reviews, source control, build processes, testing, and operations. 
• Experience in writing component-level system designs and evaluating technical feasibility trade-offs. 
• Knowledge of computer vision, image processing, SLAM or similar is a plus. 
• This position is based in Amsterdam, Netherlands. Therefore, we can only accept candidates that already live in or are in the process of relocating to this area. 

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 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 allowan