Senior Applied Scientist
On-siteAmsterdam, North Holland, The Netherlands
Job Summary
Lead the design, implementation, and integration of algorithms and ML systems that power TomTom's HD maps for ADAS, driving measurable improvements in recall, precision, and latency against hard customer targets. Own components within processing pipelines from upstream input data through algorithmic processing to validated outputs published to downstream consumers. Tackle complex technical problems at scale including noisy signals, geospatial geometry, and ground truth quality while building iteratively using agile methodologies. Mentor junior engineers, provide code reviews, and contribute to hiring within the ALF team.
Required Qualifications
- 4+ years of professional Applied Science, Machine Learning, algorithm development, or related experience
- Bachelor's degree (minimum) in Computer Science, Machine Learning, Computer Vision, Geospatial Science, Statistics, or a related quantitative field
- Solid fundamentals in algorithm design and analysis: data structures, complexity reasoning, and applied algorithms for geospatial and signal-processing problems
- Solid fundamentals in machine learning: model training and evaluation, statistics, and experimental design
- Proficiency in Python
- Experience with at least one ML framework (PyTorch, TensorFlow, or equivalent)
- Experience with at least one large-scale data processing framework (Spark, Databricks, or equivalent)
- Experience taking algorithms, ML models, or data pipelines into production
- Experience leading well-scoped projects or components to delivery with minimal guidance
- Experience mentoring junior colleagues and providing insightful code reviews
- Proficient in written and verbal communication in English
- Ability to solve complex problems on your own, taking a new perspective on existing solutions and leveraging your experience, peers, and other resources
Desired Qualifications
- Master's or PhD
- Working familiarity with algorithm design for geospatial and geometric problems (polygon geometry, map-matching, spatial indexing)
- Working familiarity with classical ML and clustering on noisy sensor data
- Working familiarity with computer vision (detection, segmentation)
- Working familiarity with data and ML pipelines at scale (training pipelines, MLOps, dataset generation)
- Curiosity and desire to learn, and to expand your skill set across the ML stack
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