Staff Computer Vision Engineer responsible for the full lifecycle of spatial AI and visual navigation features, from camera integration and image processing to high-level architectural design. This ownership-first role involves translating research into production-grade algorithms, establishing benchmarking pipelines, and ensuring robust real-time localization and mapping across diverse hardware platforms. The engineer will also mentor junior team members and contribute to technical strategy.

Responsibilities

Lead the research, evaluation, and selection of state-of-the-art computer vision, deep learning, and spatial navigation methodologies for highly accurate 3D maps of large-scale facilities, considering both internal development and third-party commercial solutions.
Develop or integrate deep learning and classical CV algorithms to extract semantic information from environments (e.g., structural elements, zones, and specific objects) for overlay onto base map.
Ensure maps can be dynamically updated over time as the physical layout of a facility changes, enabling map version management and consistency.
Design and own a rigorous benchmarking framework to continuously evaluate the accuracy, latency, compute footprint, and reliability of internal code versus off-the-shelf and vendor technologies.
Rapidly prototype new perception capabilities and architect their transition into highly optimized, edge-capable production code, or seamlessly encapsulate and integrate verified third-party modules.
Collaborate tightly with infrastructure and UI engineers to manage data products, render maps, and track assets for the end user.
Understand how and work with the larger navigation team to use camera data with GNSS, IMU, wheel odometry, and other indoor positioning signals to maintain high-confidence state estimation for moving agents in all environments.
Drive performance tuning for edge deployment to ensure tracking algorithms run with low latency and high reliability on constrained compute architectures.
Proactively identify failure modes in tracking and mapping and design robust algorithmic fallbacks.
Mentor junior engineers and establish best practices across the team.
Contribute to architecture discussions, technical strategy, and roadmap planning.

Requirements

7+ years of professional algorithm and software development experience, with significant depth in applied research, computer vision, or robotics.
Expertise in modern C++ (C++14 or later) and Python, with a demonstrated history of success of taking AI model prototypes (PyTorch, TensorFlow) and turning them into scalable, real-time production systems.
Expertise in ROS1/ROS2.
Hands-on experience with Visual SLAM, 3D reconstruction, and mapping architectures.
Experience in deploying semantic segmentation/object detection in real-world environments.
Experience with multi-view geometry, camera calibration, and fusing vision with other sensor modalities (IMU, GNSS).
Ability to take high-level research and business goals and decompose them into actionable engineering tasks, realistic schedules, and clear milestones.
MS or PhD in Computer Science, Robotics, or equivalent experience.

Nice to have

Background in deploying optimized vision models to edge devices using TensorRT, ONNX, or platform-specific accelerators.
Experience in deploying multi-object tracking and ReID architectures in real-world, dynamic environments.
Familiarity with managing large-scale point clouds, mesh generation, or NeRFs/Gaussian Splatting for environmental representation.