Machine Learning Lead

Summary

Join Panoptyc, a rapidly growing, fully remote team revolutionizing loss prevention with visual AI, as their Machine Learning Lead (R&D). In this role, you will lead a small team of engineers in building next-generation AI and computer vision products, from initial concept to market launch. You will prototype quickly, validate in the field, and guide the evolution of ambitious products, starting with theft detection in retail environments. You'll define and prioritize the technical R&D roadmap, architect scalable systems, and vet new models and deployments. This leadership position offers significant ownership and the potential to impact a company already deployed in thousands of stores. The ideal candidate will have extensive experience in AI, ML, and computer vision, a proven track record of building successful products, and strong leadership skills.

Requirements

• 8+ years in engineering roles with deep experience in AI, ML, and computer vision
• Proven track record of building 0 → 1 products or technical breakthroughs that went to market
• Strong hands-on skills with ML frameworks (e.g., PyTorch, TensorFlow), video analytics (e.g., OpenCV), and deployment tooling (e.g., ONNX, Triton, containers)
• Comfortable leading and scaling small engineering teams
• Hacker energy: fast, creative, scrappy, and resourceful
• Experience creating data pipelines for and categorizing vast sets of video training data

Responsibilities

• Lead and manage a small team of engineers to prototype and productize computer vision and AI systems
• Personally build and own early-stage proofs-of-concept, rapidly iterating and validating in the real world
• Define and prioritize the technical R&D roadmap in collaboration with product and business leads
• Architect scalable systems that transition from lab to production (edge and/or cloud)
• Vet new models, data architectures, and hardware deployments
• Be a key thought partner in setting technical direction for entirely new product lines

Preferred Qualifications

Experience with edge computing, streaming pipelines, or hardware deployment in retail, robotics, or surveillance settings