Job Summary:The Engineering Manager, State Estimation will lead a high-performing team focused on developing advanced mapping, localization, and SLAM (Simultaneous Localization and Mapping) solutions for embedded camera systems. You will oversee the research, development, and deployment of large-scale mapping and localization solutions, while shaping the technical roadmap and ensuring successful collaboration across device, cloud, and applied AI teams.
Responsibilities: - Build, mentor, and manage a team of engineers developing state-of-the-art mapping, localization, and SLAM algorithms.
- Set technical direction and project priorities, aligning them with broader organizational goals.
- Drive high-impact, cross-functional projects from conception through deployment, coordinating with research, hardware, and product teams.
- Provide technical guidance on algorithm design, system architecture, and implementation, while ensuring best practices in software development and performance optimization.
- Foster a culture of innovation, technical rigor, and collaboration across the team.
- Develop and maintain project roadmaps, milestones, and technical documentation.
- Partner with senior leadership to define long-term strategies for localization, state estimation, and multi-sensor calibration.
- Review and approve designs, architectures, and implementations, ensuring technical excellence and scalability.
- Lead recruitment, hiring, and performance management to grow a world-class engineering team.
Required Qualifications:- Bachelor of Science degree (M.S. or Ph.D. preferred) in Electrical and Computer Engineering, Robotics, Machine Learning, Computer Science, or a related field.
- 10+ years of industry experience (8+ years with an M.S., 6+ years with a Ph.D.), including significant leadership or management responsibilities.
- Proven experience leading engineering teams working on state estimation, localization, or SLAM technologies.
- Strong foundation in C++ development, geometric computer vision, stochastic processes, and nonlinear/convex optimization.
- Deep understanding of filtering algorithms (e.g., Kalman, particle filters) and optimization-based methods (e.g., nonlinear least squares).
- Familiarity with camera geometry, structure from motion, multi-sensor fusion, factor graphs, and related state estimation concepts.
- Strong track record of technical execution combined with leadership and team-building skills.
- Exceptional communication skills, with the ability to collaborate effectively across engineering, research, and product functions.
- Experience in technology transfer and delivering research innovations into production systems.
Preferred Qualifications:- Demonstrated success deploying SLAM/VIO estimators in real-world applications.
- Experience with multi-sensor systems, including GPS, IMU, camera, and wheel odometry.
- Exposure to integrating deep learning with classical state estimation approaches.
- Strong record of published research or contributions to the robotics and computer vision community.
- Experience scaling and leading multi-disciplinary engineering teams
