
Staff Applied Scientist
- Amsterdam, Noord-Holland
- Vast
- Voltijds
- Advance the automation of our HD map that already underpins multiple automotive manufacturers' advanced features.
- Make a digital twin of the real world by extracting and geolocating painted features on roads.
- Utilize multiple modalities at scale, including imagery, lidar, and sensor derived observations. These modalities are collected on streetside, aerial, and satellite platforms. TomTom, having already built maps worldwide, has a vast array of raw sources to leverage.
- Architect and optimize scalable, high-performance ML/CV based algorithms. You'll utilize computer vision techniques (segmentation, morphology), geometric processing (Structure from Motion, SLAM), and ML models to best fit the product requirements.
- Manage full project lifecycles, ensuring alignment with business goals and customer needs.
- Drive automation initiatives to streamline data processing and boost mapping efficiency.
- Provide technical leadership, guiding complex discussions, and facilitating team alignment.
- Champion iterative methodologies, fostering a culture of continuous innovation.
- Mentor junior engineers and scientists, contributing to a growth-oriented and collaborative team culture.
- Educational background: PhD or MS in Computer Vision, Machine Learning, or related fields with computer vision, computational geometry, point cloud analysis, and/or 3D reconstruction
- Professional experience: 5+ years in applied science roles, with expertise in delivering Computer Vision projects from concept to deployment.
- Algorithm expertise: Demonstrated proficiency and leadership in developing and deploying algorithms to solve practical challenges. This includes joint processing and optimization of 3D point cloud data, imagery and pose (e.g. graph and non-linear optimization).
- Sensor & mapping knowledge: Hands-on experience with sensor data processing, sensor fusion, and mapping technologies (e.g., SLAM, HD mapping, visual odometry). This includes post processing of multiple sensor systems plus GPS, IMU to produce highly accurate geo-located sources and features.
- Technical skills: Expertise in Python and Computer Vision libraries (e.g., OpenCV, COLMAP, PyTorch, TensorFlow).
- Scalability & performance: Familiarity with large-scale systems and tools like Spark and Databricks.
- Communication skills: Ability to explain complex technical concepts to cross-functional teams effectively.
- Tech stack familiarity: Working knowledge of Git, Docker, Databricks, and cloud platforms (Azure preferred).