# DRIVE Road Structure / Online Mapping / Context Fusion Engineer **Company**: NVIDIA **Experience**: senior **Job type**: full-time **Salary**: 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5 **Category**: Engineering **Industry**: Technology **Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Context-Fusion-AI-Engineer---Autonomous-Vehicles_JR2025163?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_ca160a3f-7e0 ## Description We 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. **Responsibilities:** - 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. **Requirements:** - 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 deployment. **Benefits:** - Base salary range: 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5 - Equity and benefits ## Skills ### Required - C++ - Python - PyTorch - CUDA - Transformer - VLM - multimodal perception - fusion - autonomous driving - 3D perception - deep learning ### Nice to have - BEV - point-cloud/voxel - neural scene representation - 3D reconstruction - occupancy-flow - spatiotemporal world-model methods - publications or open-source contributions in computer vision, robotics, machine learning, 3D perception, multimodal learning, or autonomous driving --- Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Context-Fusion-AI-Engineer---Autonomous-Vehicles_JR2025163?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)