Rainesdev
Head of Computer Vision
Location:
San Francisco Bay Area (On-Site, 5 Days a Week) Overview
We’re seeking an accomplished
Head of Computer Vision
to lead the development and deployment of next-generation video analytics systems that will transform how work is measured and optimized in complex, large-scale environments. You will guide technical strategy, manage research-to-production pipelines, and work hands-on to deliver breakthrough capabilities in automated video labeling, object tracking, and real-time analytics. This role offers the rare opportunity to build and scale the entire computer vision function at a fast-growing company, working at the intersection of hardware, AI, and real-world impact. You will collaborate closely with cross-functional teams, from hardware engineers to data scientists, to deploy systems that integrate seamlessly with purpose-built devices in the field. Key Responsibilities
Technical Leadership
– Define and execute the computer vision roadmap, from research to large-scale production deployment. Model Development
– Architect and optimize models for video processing, object detection, and activity recognition, ensuring robustness in real-world conditions. Pipeline & Infrastructure
– Oversee design and scaling of ML pipelines, including edge computing and integration with proprietary hardware. Product Integration
– Work with product and hardware teams to align CV capabilities with end-user requirements and operational goals. Team Building
– Recruit, mentor, and grow a high-performing computer vision engineering team. Innovation & Research
– Keep the organization at the forefront of CV advancements, publishing and patenting as opportunities arise. Qualifications
7+ years of experience in computer vision and machine learning, with at least 3 years in a high-growth startup or leading projects at a top-tier tech company. Proven track record of taking ML/CV projects from initial concept to production with measurable business impact. Strong background in video analysis, object detection, and tracking in production environments. Hands-on experience with CV frameworks such as
PyTorch ,
TensorFlow , and
OpenCV . Expertise in edge computing, hardware/software integration, and scaling video analytics solutions. Advanced degree (M.S./Ph.D.) in Computer Science, Electrical Engineering, or related field. Published work in leading CV/ML conferences (CVPR, ICCV, NeurIPS) or significant patents in the field. Preferred Experience
Applications of CV in autonomous systems, robotics, or applied AI fields. Building unique, large-scale labeled datasets and leveraging them for competitive advantage. Leading multi-disciplinary teams that bridge the gap between research and field deployment. Who You Are
Equally comfortable in the weeds coding and at the whiteboard setting strategic direction. Driven by real-world impact, especially in industries where technology adoption is accelerating. Operate with first-principles thinking and bring humility, curiosity, and a bias for action. Excited to work hands-on in the lab and on-site to see your technology in action. Benefits Full Benefits
San Francisco Bay Area (On-Site, 5 Days a Week) Overview
We’re seeking an accomplished
Head of Computer Vision
to lead the development and deployment of next-generation video analytics systems that will transform how work is measured and optimized in complex, large-scale environments. You will guide technical strategy, manage research-to-production pipelines, and work hands-on to deliver breakthrough capabilities in automated video labeling, object tracking, and real-time analytics. This role offers the rare opportunity to build and scale the entire computer vision function at a fast-growing company, working at the intersection of hardware, AI, and real-world impact. You will collaborate closely with cross-functional teams, from hardware engineers to data scientists, to deploy systems that integrate seamlessly with purpose-built devices in the field. Key Responsibilities
Technical Leadership
– Define and execute the computer vision roadmap, from research to large-scale production deployment. Model Development
– Architect and optimize models for video processing, object detection, and activity recognition, ensuring robustness in real-world conditions. Pipeline & Infrastructure
– Oversee design and scaling of ML pipelines, including edge computing and integration with proprietary hardware. Product Integration
– Work with product and hardware teams to align CV capabilities with end-user requirements and operational goals. Team Building
– Recruit, mentor, and grow a high-performing computer vision engineering team. Innovation & Research
– Keep the organization at the forefront of CV advancements, publishing and patenting as opportunities arise. Qualifications
7+ years of experience in computer vision and machine learning, with at least 3 years in a high-growth startup or leading projects at a top-tier tech company. Proven track record of taking ML/CV projects from initial concept to production with measurable business impact. Strong background in video analysis, object detection, and tracking in production environments. Hands-on experience with CV frameworks such as
PyTorch ,
TensorFlow , and
OpenCV . Expertise in edge computing, hardware/software integration, and scaling video analytics solutions. Advanced degree (M.S./Ph.D.) in Computer Science, Electrical Engineering, or related field. Published work in leading CV/ML conferences (CVPR, ICCV, NeurIPS) or significant patents in the field. Preferred Experience
Applications of CV in autonomous systems, robotics, or applied AI fields. Building unique, large-scale labeled datasets and leveraging them for competitive advantage. Leading multi-disciplinary teams that bridge the gap between research and field deployment. Who You Are
Equally comfortable in the weeds coding and at the whiteboard setting strategic direction. Driven by real-world impact, especially in industries where technology adoption is accelerating. Operate with first-principles thinking and bring humility, curiosity, and a bias for action. Excited to work hands-on in the lab and on-site to see your technology in action. Benefits Full Benefits