Staff Software Engineer

Overview At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges. From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future. What the Job Involves As a Staff Software Engineer on the Perception team, you will be a key technical leader, defining and driving the long-term vision and architecture for our perception systems in forthcoming pilots, directly influencing Hayden’s mission and roadmap for business expansion. This role requires deep expertise in computer vision and/or robotics algorithms that are deployed on the edge/cloud. You will be responsible for setting the technical direction and standards for the team, architecting complex, scalable, and robust end-to-end perception and robotics systems for deployment on real-world hardware, and ensuring their successful integration into Hayden’s core product platform. This is a C++ software engineering position demanding both hands-on technical mastery and significant architectural and systems leadership. You will operate with complete technical ownership over major system components, mentoring junior and senior engineers, driving complex cross-functional initiatives, and making critical trade-off decisions that balance bleeding-edge innovation, system reliability, performance, and long-term maintainability across the entire Perception stack. Responsibilities Spearhead the architectural design, implementation, and long-term ownership of next-generation perception systems, ensuring seamless transition from research prototypes to robust, scalable production solutions. Champion best practices in software development, delivering high-performance, meticulously tested, and maintainable C++ code optimized for heterogeneous edge and robotics computing platforms. Architect and optimize high-throughput, real-time perception pipelines, setting the technical direction for advanced techniques in object detection, tracking, and sophisticated sensor fusion Drive the strategic selection, adaptation, and integration of state-of-the-art Machine Learning (ML) and Computer Vision (CV) models, including the development of novel, proprietary models tailored to complex, large-scale Hayden-specific problems. Provide technical leadership in highly ambiguous and complex problem domains, defining the technical roadmap and consistently striking the optimal balance between rapid iteration for R&D and the rigorous requirements for productization and scale. Act as a key technical liaison, collaborating strategically with Product leadership and setting technical standards across cross-functional Engineering organizations. Define and significantly contribute to foundational shared infrastructure, tooling, and architectural patterns, scaling pilot initiatives into core, mission-critical product capabilities. Required Qualifications Advanced degree (MS or PhD) in Computer Science, Electrical Engineering, Robotics, or a related field. 10+ years of relevant experience in building and deploying perception systems; experience in automotive or robotics domains is a plus. Deep expertise in one or more of: robotics, state estimation, computer vision, or applied machine learning. Extensive experience leading large, complex, production-grade systems end-to-end. Expert-level proficiency in modern C++, including experience with real-time and/or embedded systems. Proven track record of technical leadership and architectural ownership across multiple projects or teams. Experience scaling systems from prototype to production in ambiguous, fast-moving environments. Strong ability to influence without authority and drive alignment across teams. Demonstrated mentorship and ability to raise the bar for engineering quality across an organization. Compensation Compensation Range: $230,522 - $299,679 #J-18808-Ljbffr