Senior Context Fusion AI Engineer - Autonomous Vehicles

Location: Northern

Senior Context Fusion AI Engineer
- Autonomous Vehicles page is loaded## Senior Context Fusion AI Engineer
- Autonomous Vehicles locations: US, CA, Santa Clara: US, NV, Remote: US, WA, Redmond: US, WA, Seattletime type:
Full time posted on:
Posted Yesterday job requisition  are looking for a strong engineer to join the DRIVE Road Structure / Online Mapping / Context Fusion team. In this role, you will help craft and guide the future of our L3/L4 autonomous-driving solution by building a complete, learned 3D/4D world model that fuses navigation, ego-motion, perception, and sensor signals. You will work closely with perception, prediction, planning, and simulation teams to deliver a world representation that is complete, temporally consistent, uncertainty-aware, and robust enough to drive through the most challenging roads and intersections in L3/L4 autonomy level.**!
**** What You'll Be Doing:
*** Design and develop learning-based, multimodal sensor-fusion systems that transform synchronized sensor history, ego-motion, navigation context, and driving context into a unified spatiotemporal world representation.
* Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs, with appropriate handling of calibration, synchronization, coordinate transforms, sensor latency, and uncertainty.
* Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation, including; road graph elements such as lanes, boundaries, crosswalks, and traffic controls; semantic scene understanding; occupancy and free-space representations, including uncertain and occluded regions;
* Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion; BEV, point/voxel, and image-based representations; temporal context aggregation; and cross-modal attention.
* Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets. Define multi-task objectives and metrics that balance perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior.
* Investigate foundation-model approaches for autonomous driving, including vision-language models, multimodal pre-training, representation learning, and efficient deployment of learned world models.
* Work closely with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to convert research advances into robust, production-quality AV systems.
* Develop systematic analysis and debugging tools for model failures, cross-sensor disagreement, long-tail scenarios, distribution shift, and regressions in closed-loop simulation and on-road evaluation.
** What We Need To See:
*** 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 is highly valued.
* 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…