Senior Perception Engineer, Obstacle Foundation Models, Autonomous Vehicles

What you'll do

• Develop and improve the technical design, architecture, and roadmap for 3D obstacle perception in autonomous driving systems.
• Design and implement advanced 3D perception models using multi-camera inputs and multi-sensor fusion like radar and lidar.
• Build production-grade deep learning models using CNNs, transformers, large-scale pretraining, and parameter-efficient fine-tuning techniques.
• Define and maintain KPI frameworks to quantify performance and analyze large datasets to identify and fix failure modes.
• Develop data strategies by specifying labeling requirements and prioritizing data collection for model improvement.
• Implement model-assisted workflows including active learning, auto-labeling, and vision-language models.
• Ensure perception solutions meet strict product requirements for safety, latency, resource usage, and software robustness.

What we're looking for

• PhD with 4+ years, MS with 6+ years, or BS with 8+ years of experience in Computer Science, Computer Engineering, or a related field.
• Hands-on experience developing deep learning-based perception systems for complex real-world problems.
• Proficiency in PyTorch and a track record of moving models from prototype to production.
• Experience in data-driven development including collaboration on data strategy, labeling quality, and iterative model improvement.
• Strong programming skills in Python and/or C++ to build high-performance, production-quality software.
• Expertise in 3D computer vision fundamentals, including camera modeling, calibration, multi-view geometry, and 3D representations.
• Experience with CNNs, transformers, large-scale pretraining, and parameter-efficient fine-tuning like LoRA.
• Experience deploying DNN-based perception pipelines on embedded or real-time platforms optimized for latency and memory.