Staff Computer Vision Engineer
- Compensation
- $215.3K–$265.8K / yr
- Location
- San Francisco HQ
- Work setup
- Hybrid
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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 sys
- 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.
Full description
About Point One Navigation
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 FusionEngine™ 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.
Role Outcome
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.
FusionEngine 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.
Success in this role means:
- 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.
Immediate Areas of Focus
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.