Computer Vision Engineer

Staff Computer Vision Engineer

Point One Navigation is on a mission to bridge the digital and physical worlds through precision location, with an API-first, developer-focused approach. Our RTK corrections network and Fusion Engine™ software deliver centimeter-level accuracy and high-confidence positioning for vehicles, robots, drones, and devices across industries in outdoor applications. We are actively broadening our expertise into indoor environments to provide the same high-standard localization and navigational quality for users everywhere.

Staff Computer Vision Engineers are responsible for the comprehensive lifecycle of Point One's spatial AI and visual navigation features, overseeing everything from initial camera integration and image processing to high-level architectural and algorithmic design.

This is an ownership-first role: you will conceptualize and drive complex technical challenges end-to-end - from early architecture through deployment in mission-critical systems - while raising the technical bar across the team.

Fusion Engine already powers a wide range of devices, hardware platforms, and customer applications. The R&D team is responsible for making sure our vision and perception systems work reliably across all of them:
Designing solutions robust to visually challenging environments, optimizing models for compute-constrained edge devices, and ensuring our algorithms stay thoroughly tested, verified, and production-ready as we scale.

• State-of-the-art CV and SLAM techniques are successfully translated from research papers, internal prototypes, or third-party solutions into highly performant, production-grade algorithms.

• Rigorous benchmarking pipelines are established to objectively evaluate internal algorithms against commercial OTS solutions and vendor offerings.

• Vision pipelines automatically generate and maintain accurate, semantically rich maps of complex indoor environments with minimal manual intervention.

• Real-time localization and multi-agent tracking (assets, robots, people) are highly robust, minimizing latency and identity switches even in dynamic or visually degraded conditions.

• Spatial data, coordinate frames, and map layers are exposed via clean data models and APIs, empowering our UI and infrastructure teams to build seamless user-facing applications.

• Junior engineers grow faster and the team's practices improve measurably over time.

Applied Research, Benchmarking & Selection

• 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.

Drive Real-Time Localization and Tracking

• 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.

Raise the Technical Bar

• Mentor…