Senior Perception Engineer – Obstacle Foundation Models, Autonomous Vehicles

Job Description:

• Develop and improve the technical design, architecture, and roadmap for 3D obstacle perception supporting end-to-end autonomous driving
• Design and implement advanced 3D perception models using multi-camera inputs and/or multi-sensor fusion for obstacle detection and tracking
• Build efficient, production-grade deep learning models by defining objectives, selecting and prototyping architectures, running experiments, and applying training and evaluation best practices
• Define and maintain KPI frameworks to quantify perception performance
• Analyze large-scale real and synthetic datasets to identify failure modes and improve accuracy, robustness, and efficiency
• Contribute to perception data strategy, including data and labeling requirements, collection and annotation priorities, and model-assisted workflows
• Collaborate with data and ground-truth teams on active learning, auto-labeling, vision-language models, and model-in-the-loop tooling
• Collaborate with safety, systems, and software teams to meet product requirements for safety, latency, resource usage, and software robustness
• Prepare perception solutions for deployment at scale

Requirements:

• PhD with 4+ years, MS with 6+ years, or 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
• Experience in data-driven development, including collaboration with data, labeling, and ground-truth teams
• Strong programming skills in Python and/or C++
• Experience building reliable, high-performance, production-quality software
• Experience designing and deploying 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 for latency, memory, and compute constraints
• Familiarity with CNNs, transformers, large-scale pretraining, parameter-efficient fine-tuning, 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 fundamentals, camera modeling and calibration, multi-view geometry, and 3D representations
• Experience with CUDA development and GPU-accelerated components
• Excellent communication and collaboration skills

Benefits:

• Equity
• Benefits