Sensors and Vision Engineer

In charge of designing, training, and deploying machine learning models for perception and automated data annotation at scale for autonomy systems.

Responsibilities

• Develop novel formulations and architectures for a wide variety of computer vision tasks using RGB camera, lidar and/or radar sensors.
• Perform large-scale distributed training of deep neural networks to build a unified and consistent vector space for autonomous trajectory planning tasks (e.g., occupancy, occupancy flow, semantics, geometry, detection, geometric equivalent surface).
• Design metrics, tasks, and datasets that aid in perception and autonomy.

Criteria

• Strong experience writing production-level Python and software engineering best practices.
• Solid mathematical fundamentals, including linear algebra, vector calculus, probability theory, and numeric optimization.
• Understanding of deep learning: layer details, loss functions, optimization, etc
• Understanding of modern deep learning techniques (CNNs, transformers, autoregressive models, etc.).
• Domain expertise in at least one of these areas: object detection & tracking, pose estimation, depth estimation, semantic & instance segmentation, video models, differentiable rendering, Neural Radiance Field (NeRF), 3D reconstruction, visual SLAM, structure from motion.
• Familiarity with basic computer vision concepts such as intrinsic and extrinsic calibrations, homogeneous coordinates, projection matrices, and epipolar geometry.