Senior Context Fusion AI Engineer - Autonomous Vehicles

Originally posted 18 September 2026 by the employer — open 1 day.

About the role

This role involves building a complete, learned 3D/4D world model that fuses navigation, ego-motion, perception, and sensor signals for L3/L4 autonomous-driving solutions.

What you'll do

• Design and develop learning-based, multimodal sensor-fusion systems for a unified spatiotemporal world representation.
• Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs.
• Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation.
• Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion.
• Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets.
• Investigate foundation-model approaches for autonomous driving, including vision-language models and multimodal pre-training.

What you'll need

• BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field, or equivalent experience.
• 8+ years of experience, with at least 2+ years in the AV or robotics industry and 2+ years of leadership experience in a technically area.
• Strong experience developing production-quality sensor-fusion, perception, state-estimation, or autonomous-driving systems.
• Demonstrated experience with learning-based multimodal perception or fusion involving two or more cameras, LiDAR, radar, map, navigation, and ego-motion signals.
• Solid understanding of 3D geometry, coordinate frames, calibration, temporal synchronization, ego-motion compensation, tracking, uncertainty estimation, and sensor failure modes.
• Experience with deep-learning methods for 3D perception, lego-context scene representation, occupancy/occlusion prediction, semantic segmentation, object detection/tracking, motion prediction, or planning.
• Strong C++ and Python programming skills, with hands-on experience developing, training, and optimizing deep-learning models in PyTorch. Experience with CUDA, distributed training, mixed-precision techniques, and efficient GPU inference using NVIDIA software and hardware.
• Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference.
• Experience training and evaluating models at scale, including distributed training, dataset curation, offline evaluation, simulation-based validation, and production monitoring.
• Ability to work across research and engineering boundaries: turn an ambiguous AV problem into measurable technical objectives, build the solution, and drive it to deployment.

Skills: L3/L4 autonomous-driving, multimodal sensor-fusion, deep-learning methods, Transformer-based, production-quality AV systems