Senior Context Fusion AI Engineer - Autonomous Vehicles
Remote • US, CA, Santa Clara
Posted 1mo ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
We are seeking a skilled engineer to join our Autonomous Vehicles team, focusing on the L3/L4 autonomous-driving solution. You will be instrumental in developing a comprehensive, learned 3D/4D world model by fusing navigation, ego-motion, perception, and sensor signals. This role involves close collaboration with perception, prediction, planning, and simulation teams to create a world representation that is complete, temporally consistent, uncertainty-aware, and robust for challenging driving scenarios. Our vision is to develop a shared multimodal scene representation that supports road understanding, 3D object perception, motion prediction, and planning, aiming to achieve human-level AI for navigation in unconstrained environments.
Responsibilities
- Design and develop learning-based, multimodal sensor-fusion systems to create a unified spatiotemporal world representation.
- Build architectures that reason over camera, LiDAR, radar, and vehicle-state inputs, handling calibration, synchronization, and uncertainty.
- Develop end-to-end and multi-task models for driving-relevant outputs like road graph elements, semantic scene understanding, and occupancy representations.
- Create scalable multimodal fusion architectures, including Transformer-based fusion, BEV, and temporal context aggregation.
- Develop training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets, defining objectives and metrics.
- Investigate foundation-model approaches for autonomous driving, including multimodal pre-training and efficient deployment.
- Collaborate with cross-functional teams to translate research into production-quality AV systems.
- Develop tools for analyzing model failures, sensor disagreement, and distribution shifts.
Requirements
- BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field, or equivalent experience.
- 8+ years of experience, with at least 2+ years in AV or robotics and 2+ years of technical leadership.
- Strong experience developing production-quality sensor-fusion, perception, state-estimation, or autonomous-driving systems.
- Demonstrated experience with multimodal perception or fusion involving multiple sensor types (cameras, LiDAR, radar, etc.).
- Solid understanding of 3D geometry, coordinate frames, calibration, synchronization, and uncertainty estimation.
- Experience with deep-learning methods for 3D perception, scene representation, occupancy prediction, and object tracking.
- Strong C++ and Python programming skills, with hands-on experience training and optimizing deep-learning models in PyTorch.
- Experience with CUDA, distributed training, mixed-precision techniques, and efficient GPU inference.
- Experience with Transformer, VLM, or multimodal foundation-model architectures (pre-training, fine-tuning, distillation, quantization).
- Experience training and evaluating models at scale, including distributed training and production monitoring.
- Ability to bridge research and engineering, defining technical objectives and driving solutions to deployment.