Senior Perception Engineer, Obstacle Foundation Models - Autonomous Vehicles
$184,000–$287,500 year
On-siteSanta Clara, California, United States
Job Summary
Develop and improve the technical design, architecture, and roadmap for 3D obstacle perception to support end-to-end autonomous driving functionalities. Design and implement advanced 3D perception models using multi-camera inputs and multi-sensor fusion for obstacle detection and tracking. Build efficient, production-grade deep learning models by defining objectives, prototyping architectures, and running experiments using techniques like large-scale pretraining and parameter-efficient fine-tuning. Help define KPI frameworks to quantify performance and analyze datasets to identify failure modes. Contribute to the data strategy by specifying labeling requirements and collaborating with ground-truth teams on model-assisted workflows. Collaborate with safety, systems, and software teams to ensure solutions meet product requirements for safety, latency, and resource usage.
Required Qualifications
- PhD with 4+ years
- MS with 6+ years
- BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field
- Hands-on experience developing deep learning–based perception or closely related systems for complex real-world problems
- Strong proficiency in frameworks such as PyTorch
- Track record of taking models from prototype to production
- Proven experience in data-driven development, including close collaboration with data, labeling, and ground-truth teams on data strategy, labeling quality, and iterative model improvement
- Strong programming skills in Python and/or C++
- Experience building reliable, high-performance, production-quality software
- Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary teams
Desired Qualifications
- Experience designing and deploying perception solutions for autonomous driving or robotics using camera-based deep learning at scale
- Hands-on experience architecting and deploying DNN-based perception pipelines on embedded or real-time platforms, including optimization for latency, memory, and compute constraints
- Experience with modern architectures such as CNNs and transformers
- Familiarity with techniques like large-scale pretraining, parameter-efficient fine-tuning (e.g., LoRA), or vision-language models (VLMs)
- Strong publication record or recognized contributions in deep learning, computer vision, or autonomous systems at leading conferences/journals (e.g., CVPR, ICCV, NeurIPS, IROS)
- Deep understanding of 3D computer vision fundamentals, including camera modeling and calibration (intrinsic and extrinsic), multi-view geometry, and 3D representations
- Experience applying 3D computer vision concepts in transformer-based 3D or BEV perception pipelines
- Experience with CUDA development and optimizing training or inference pipelines through custom CUDA kernels or other GPU-accelerated components
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