Senior Engineer, Computer Vision (Remote - US)

Compound Eye
United States - Remote$155k – $205kPosted 1 April 2026

Job Description

Senior Engineer, Computer Vision (Remote - US) Compound Eye enables machines to understand their surroundings in 3D and in real time using only passive sensors like cameras and IMUs. Our VIDAS™ technology takes video from automotive-grade cameras and outputs a dense, semantic 3D representation of the world that vehicles and robots use to navigate their environment—a fully redundant alternative to LiDAR and radar. We're a revenue-generating company shipping product to customers across automotive, agriculture, healthcare, and defense, and backed by Khosla Ventures and other tier-one investors. We’re a US team of 20 with a mission to teach machines to see. Our culture is built on transparency, mutual respect, and valuing great ideas wherever they come from. THE ROLE We’re looking for a Senior Computer Vision Engineer with deep expertise in state estimation, sensor fusion, and geometry-based perception. You’ll work at the core of VIDAS™—building and advancing the algorithms that let machines understand the world through cameras alone. The hardest problems here don’t have off-the-shelf solutions. You’ve been in situations where the algorithm failed, the literature didn’t cover it, and you had to work back to first principles to figure out why. WHAT YOU’LL DO - Design and advance algorithms for pose estimation, SLAM, Visual-Inertial Odometry (VIO), and 3D reconstruction - Build and tune navigation filters including EKF and other classical state estimation approaches - Own sensor fusion pipelines that combine IMU and camera data in lidar-free setups - Develop and maintain multi-view geometry and camera calibration systems - Drive real-time perception solutions for autonomy and robotics applications - Analyze algorithm performance and failure modes, and translate findings into concrete improvements - Help grow and mentor a team of similarly-qualified engineers WHAT YOU BRING 3–6+ years of hands-on experience in computer vision or robotics perception, with depth in: - Pose estimation, SLAM, Visual-Inertial Odometry (VIO), and 3D reconstruction - Nonlinear optimization (e.g., bundle adjustment, factor graphs) - Classical state estimation and Kalman filters (EKF) - Sensor fusion combining IMU and cameras in lidar-free environments - Multi-view geometry and camera calibration - Real-time perception systems for autonomous vehicles or robotics You’ve derived or adapted algorithms, not just applied them. You’re comfortable with manifold optimization, uncertainty propagation, and error-state representations. When something breaks, you go back to the math. Strong foundations in: - Linear algebra, probability, and estimation theory - Analyzing numerical data and debugging algorithm behavior TRAVEL This role is fully remote and open to anyone based in the United States. You’ll travel to Redwood City, CA four times per year for company on-sites, and up to three additional times per year for conferences or customer demos. All travel is covered by Compound Eye. Compensation & Benefits Base salary: $155,000 to $205,000, depending on experience. - Strong Incentive Stock Options (ISO) - Medical, dental, and vision insurance - Annual home office stipend - Employer-paid long-term disability, short-term disability, and life insurance - 401(k) with employer match after 6 months - Flexible PTO and 8 holidays + 2 week winter break - Paid family leave - Quarterly onsites in San Francisco We're looking for a great engineer to join our US team of 20. Our mission is to teach machines to see. Our culture is based on transparency, mutual respect, mutual accountability, and valuing great ideas no matter where they come from. We already have revenue and our Series A round was led by a tier one VC. We are a remote-first company with employees across the United States. We had remote employees long before the pandemic and our team is now distributed across three states. We will ensure you have everything you need to work from home ... (truncated, view full listing at source)
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