Director of Computer Vision

• The Director of Computer Vision will drive the technical vision, research and engineering execution for the platform, with focus on the following core areas:
• Foundational Research Strategy: Define the long-term research agenda for fundamental computer vision capabilities and drive the culture of scientific excellence
• Technical Vision, Engineering Leadership, and Execution: Provide executive technical leadership to a unified research and engineering organization, defining the technical architecture, strategy, and engineering roadmap for the Computer Vision Platform. Drive execution, ensuring the seamless translation of product requirements into robust technical specifications and successful delivery of next-generation visual intelligence solutions that are highly scalable and align with overall product and business strategy
• Organizational Leadership and Talent Strategy: Manage and scale a unified computer vision research and engineering organization distributed across multiple geographies, overseeing multiple teams, and defining the strategy for recruiting, mentoring, and attracting world-class talent
• End-to-End MLOps and Deployment: Own the entire engineering lifecycle for central, reusable computer vision models and foundational AI infrastructure. This includes establishing best-in-class MLOps practices for scalable training, efficient deployment, continuous monitoring, and performance optimization across edge and cloud environments
• Performance Measurement and Continuous Improvement: Define, implement, and track key technical performance metrics (e.g., latency, throughput, model efficiency, system reliability) to measure engineering success, identify bottlenecks, and drive continuous improvement in execution and delivery
• Technical Innovation and Risk Management: Evaluate and integrate cutting-edge computer vision research and technologies. Proactively identify and mitigate significant technical risks, and lead critical engineering decisions, including build-versus-buy analysis

Benefits

• Flexible work models
• Paid time off
• Paid parental and family leave
• Health care benefits
• Global wellness resources
• Employee assistance programs
• Rotation programs
• Mentor relationships
• Learning and development opportunities
• Retirement benefits
• Employee bonuses
• Stock grants & employee stock purchase plans- Domain Knowledge: Experience in public safety, security-focused software (e.g., video security, evidence management), mission-critical systems, or emergency communications is a plus
• Start-up Mentality & Agility: Experience in a start-up or fast-paced environment is highly valued, demonstrating high ownership, bias for action, comfort with ambiguity, and the ability to drive 0-to-1 product development with limited resources
• 10+ years of technical leadership experience leading computer vision teams and organizations, with a focus on building and deploying enterprise-scale platforms and solutions in production
• Technical Acumen & Deep Expertise: Deep, demonstrable expertise in computer vision, machine learning algorithms, and the end-to-end MLOps lifecycle, including 5+ years of hands-on experience building and optimizing computer vision models or as a computer vision researcher. Proven ability to engage in complex technical discussions, define the architectural vision for central, reusable AI infrastructure and models, and drive technical strategy while managing critical trade-offs
• Customer-Driven Technical Leadership (Pathfinding): Track record of translating highly ambiguous product concepts and complex customer needs into clear, executable technical roadmaps and architectural specifications
• Organizational & People Leadership: Demonstrated ability to manage and scale a unified, distributed engineering and research organization of 50 to 100 people, mentor senior technical talent, and lead multiple teams across different geographies
• Strong Analytical and Research Skills: Hands-on experience driving innovation through foundational research, working with AI performance metrics (e.g., Precision/Recall), real-time video processing, and inference optimization
• Executive Technical Strategy & Metrics: Proven experience defining long-term technical vision, engineering strategy, and roadmaps for a large-scale platform. Expertise in defining and implementing technical metrics (e.g., latency, throughput, system reliability, model efficiency) to measure engineering excellence and drive continuous improvement across an organization