Principal Perception Engineer – Obstacle Foundation Models, Autonomous Vehicles

Job Description:

• Own the technical vision, architecture, and roadmap for 3D obstacle perception supporting end-to-end autonomous driving
• Design and develop 3D perception models using multi-camera inputs and/or camera, radar, and lidar sensor fusion
• Lead development of efficient, production-grade deep learning models
• Define objectives, select architectures, guide experimentation, and establish training and evaluation best practices
• Define and drive KPI frameworks for perception performance
• Analyze large-scale real and synthetic datasets to identify failure modes and improve accuracy, robustness, and efficiency
• Lead perception data strategy, including data and labeling requirements, collection, annotation, active learning, auto-labeling, VLMs, and model-in-the-loop tooling
• Partner with safety, systems, and software teams to meet product requirements for safety, latency, resource usage, and software robustness
• Drive deployment readiness at scale
• Provide technical leadership and mentorship across perception and autonomy teams

Requirements:

• 15+ years of 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
• Demonstrated technical leadership as a senior or principal-level individual contributor
• Experience owning features or subsystems end-to-end, setting technical direction, making architectural decisions, and coordinating across teams
• Proven experience in data-driven development, including collaboration with data, labeling, and ground-truth teams
• Strong programming skills in Python and/or C++
• History of building reliable, high-performance, production-quality software
• Excellent communication and collaboration skills
• BS/MS/PhD in Computer Science, Electrical Engineering, or related fields, or equivalent experience
• Experience leading perception solutions for autonomous driving or robotics using camera-based deep learning at scale
• Experience architecting and deploying DNN-based perception pipelines on embedded or real-time platforms
• Experience optimizing latency, memory, and compute constraints
• Familiarity with CNNs, transformers, large-scale pretraining, parameter-efficient fine-tuning such as LoRA, and vision-language models
• Strong publication record or recognized contributions in deep learning, computer vision, or autonomous systems
• Deep understanding of 3D computer vision, camera modeling and calibration, multi-view geometry, and 3D representations
• Experience with CUDA development and GPU-accelerated components

Benefits:

• Equity
• Benefits