Senior Context Fusion AI Engineer – Autonomous Vehicles

🔥 12 hours ago

🏄 California, Nevada, +1 more states – Remote

🏄 California – Remote 🎰 Nevada – Remote ☕ Washington – Remote

💵 $184k - $356.5k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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 while handling calibration, synchronization, coordinate transforms, sensor latency, and uncertainty • Develop end-to-end and multi-task models for road graph elements, semantic scene understanding, occupancy and free-space representations, including uncertain and occluded regions • Develop scalable multimodal fusion architectures using 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 balancing 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 • Collaborate with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to turn research advances into production-quality AV systems • Develop 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, including at least 2+ years in the AV or robotics industry and 2+ years of technical leadership experience • Strong experience developing production-quality sensor-fusion, perception, state-estimation, or autonomous-driving systems • Experience with learning-based multimodal perception or fusion involving two or more cameras, LiDAR, radar, map, navigation, and ego-motion signals • 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, scene representation, occupancy/occlusion prediction, semantic segmentation, object detection/tracking, motion prediction, or planning • Strong C++ and Python programming skills • Hands-on experience developing, training, and optimizing deep-learning models in PyTorch • 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 turn ambiguous AV problems into measurable technical objectives, build solutions, and drive them to deployment • Experience with CUDA, distributed training, mixed-precision techniques, and efficient GPU inference using NVIDIA software and hardware is highly valued • Experience with BEV, point-cloud/voxel, neural scene representation, 3D reconstruction, occupancy-flow, or spatiotemporal world-model methods is a plus • Publications or open-source contributions in computer vision, robotics, machine learning, 3D perception, multimodal learning, or autonomous driving are a plus • Experience optimizing models for automotive-grade real-time deployment using NVIDIA GPUs, TensorRT, CUDA, or edge inference toolchains is a plus

🏖️ Benefits

• Equity • Benefits