Senior Perception Engineer – Obstacle Foundation Models, Autonomous Vehicles

This is a fully remote position, open to applicants in California, +3 more states.

📋 Description

• Develop and enhance the technical design, architecture, and roadmap for 3D obstacle perception that supports end-to-end autonomous driving.

• Create and implement sophisticated 3D perception models utilizing multi-camera inputs and/or multi-sensor fusion for the purpose of obstacle detection and tracking.

• Construct efficient, production-ready deep learning models by defining objectives, selecting and prototyping architectures, conducting experiments, and applying best practices for training and evaluation.

• Establish and maintain KPI frameworks to measure perception performance.

• Analyze extensive real and synthetic datasets to uncover failure modes and enhance accuracy, robustness, and efficiency.

• Contribute to the perception data strategy, which includes data and labeling needs, collection and annotation priorities, and model-assisted workflows.

• Work collaboratively with data and ground-truth teams on active learning, auto-labeling, vision-language models, and model-in-the-loop tools.

• Partner with safety, systems, and software teams to fulfill product requirements regarding safety, latency, resource usage, and software reliability.

• Prepare perception solutions for large-scale deployment.

⛳️ 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.

• Practical experience in developing deep learning-based perception or closely related systems for complex real-world challenges.

• Strong expertise in frameworks such as PyTorch.

• Proven track record of advancing models from prototype to production.

• Experience in data-driven development, including collaboration with data, labeling, and ground-truth teams.

• Proficient programming skills in Python and/or C++.

• Demonstrated experience in building reliable, high-performance, production-quality software.

• Experience in 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.

• Familiarity with optimizing for latency, memory, and computational constraints.

• Knowledge of CNNs, transformers, large-scale pretraining, parameter-efficient fine-tuning, LoRA, and vision-language models.

• Strong publication record or recognized contributions in the areas of 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 abilities.

🏝️ Benefits

• Equity

• Benefits