Hybrid ADAS ML Scientist: 3D Perception & Transformers

TomTom; Amsterdam; Híbrido; EUR 90.000 - 140.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Personal development budget

Paid learning days

Parental leave

Home office setup budget

Global mobility options

Descripción de la vacante

TomTom is seeking a machine learning applied scientist to advance state-of-the-art algorithms for in-vehicle spatial awareness. You will contribute to models handling 3D map data, camera, LiDAR, and RADAR, with Transformer-based techniques and end-to-end pipelines.

The role blends research with production readiness in a fast-moving ADAS/ADS team. You will collaborate with engineers, data scientists, and product teams, running experiments, evaluating results, and translating research into

Formación

• 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 building and deploying large-scale ML models.

Responsabilidades

• Develop a solid understanding of the team's physical AI roadmap and contribute ideas that improve algorithms.
• 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 requirements.
• Apply Transformer-based and representation learning techniques within established frameworks.

Conocimientos

Machine Learning

Vision Transformers

PyTorch

Data‑driven mindset

Fast-paced environment

Communication

Herramientas

PyTorch

3D data representations

Transformers

Descripción del empleo

TomTom is seeking a machine learning applied scientist to advance state-of-the-art algorithms for in-vehicle spatial awareness. You will contribute to models handling 3D map data, camera, LiDAR, and RADAR, with Transformer-based techniques and end-to-end pipelines.

The role blends research with production readiness in a fast-moving ADAS/ADS team. You will collaborate with engineers, data scientists, and product teams, running experiments, evaluating results, and translating research into