Machine Learning Applied Scientist - ADAS Online

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 a machine learning applied scientist, you will contribute to the development and improvement of models within the in-vehicle spatial awareness stack. Work hands-on across data, experimentation, and evaluation pipelines, and grow your ability to tackle increasingly complex engineering challenges in a fast-moving team.

This 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.

What You’ll Do

• Develop a solid understanding of the team's physical AI roadmap and contribute ideas that improve algorithms within your area of ownership;
• 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 defined requirements;
• Apply Transformer-based and representation learning techniques within established frameworks, building confidence in large-scale model development;
• Run experiments and benchmarks with guidance, producing clear results and contributing to the team's evaluation practices;
• Participate in translating research into production, learning what makes ML solutions reliable and scalable;
• Actively seek feedback from senior engineers and apply it to continuously improve the quality of your work.

What You'll Need

• 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 what is needed to build and deploy large-scale ML models;
• Strong ownership and bias toward execution;
• Data-driven mindset with rigorous evaluation practices;
• Ability to operate effectively in fast-paced environments;
• Clear communication of technical trade-offs;
• High standards for code quality and reproducibility;
• Comfortable navigating ambiguity in complex technical domains.

Nice to Have

• Experience in autonomous systems or robotics perception;
• Publications or patents in machine learning or perception;
• Experience with 3D data representations (meshes, point clouds, BEV, voxel grids).