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Senior Context Fusion AI Engineer - Autonomous Vehicles

NVIDIA· Engineer, Sys SW
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Hybrid (Santa Clara, CA; Redmond, WA; Remote; Seattle, WA)$184k - $356.5kfull-timePosted 4w ago

About the role

Senior AI/ML engineer focused on learning-based, multimodal sensor fusion to build a unified spatiotemporal world model for L3/L4 autonomous driving, integrating navigation, ego-motion, perception, and sensor signals across perception, mapping, prediction, planning, and simulation teams.

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 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 (road graph elements, semantic scene understanding, occupancy/free-space representations including uncertain/occluded regions).
  • Develop scalable multimodal fusion architectures (Transformer-based early/late/hierarchical fusion; BEV, point/voxel, image-based representations; temporal context aggregation; cross-modal attention).
  • Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets; define multi-task objectives and metrics balancing perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior.
  • Investigate foundation-model approaches for autonomous driving (vision-language models, multimodal pre-training, representation learning, efficient deployment).
  • Collaborate with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to deploy robust 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.

Qualifications

  • BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field, or equivalent experience.
  • 8+ years of experience, including at least 2+ years in the AV or robotics industry and 2+ years of leadership experience in a technical area.
  • Strong experience in 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, context scene representation, occupancy/occlusion prediction, semantic segmentation, object detection/tracking, motion prediction, or planning.
  • Strong C++ and Python programming skills; hands-on experience with PyTorch; experience with CUDA, distributed training, mixed-precision, and NVIDIA deployment tools is highly valued.
  • Experience with Transformer, VLM, or multimodal foundation-model architectures (pre-training, fine-tuning, distillation, quantization, efficient inference).
  • Experience training and evaluating models at scale (distributed training, dataset curation, offline evaluation, simulation-based validation, production monitoring).
  • Ability to translate ambiguous AV problems into measurable technical objectives and drive to deployment.

Skills

Required:Multimodal sensor fusion3D perception and geometryDeep learning (PyTorch)C++ and PythonTransformer / multimodal models

Benefits

  • equity
  • benefits (as linked in posting)
Last updated Sep 25, 2026. Interested? Apply on the company site.

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