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Success Matcher Recruitment partners with a trailblazing AI-driven retail security startup in New York to recruit a Founding Applied Scientist who will build core AI capabilities from the ground up. You will own the full model development lifecycle, from data collection strategy to training, optimization, and shipping production-grade code, reporting to the CTO.
You will lead model development, bridge research and engineering with production-ready Python code, and design data collection
Our client is an innovative AI-driven retail security firm currently experiencing 10x growth. As they scale from individual retailers to massive enterprise clients with 100+ locations, they are seeking a Founding Applied Scientist to build their core AI capabilities from the ground up.
This is not a "pure research" role. You will be a hands-on builder owning the full model development lifecycle. You’ll be responsible for everything from data collection strategy and quality assessment to training, inference optimization, and shipping production-quality code. Reporting directly to the CTO, you will collaborate with a distributed engineering team to deliver computer vision solutions that have an immediate impact on retail safety and operations.
Lead Model Development: Take full ownership of the model lifecycle, including production training, optimization, and inference deployment.
Bridge Research & Engineering: Write substantial, production-ready code (Python) to ensure CV models are successfully deployed via cross-functional engineering teams.
Strategic Data Planning: Design and implement data collection and quality assessment frameworks to improve model accuracy in real-world retail environments.
Technical Communication: Present complex technical findings and AI roadmaps to both engineering teams and executive leadership.
Experience: 3+ years of professional experience in Applied Science, ML Engineering, or Computer Vision roles.
Proven Track Record: You have shipped customer-facing products and have deep experience with real-world CV applications (e.g., YOLO or similar architectures), rather than just academic projects.
Technical Mastery: Advanced proficiency in Python with PyTorch or TensorFlow.
Academic Background: Master’s degree or PhD in Computer Science, Machine Learning, or Computer Vision.
Collaborative Spirit: Comfortable working in a fast-paced startup environment and collaborating with distributed teams across time zones (specifically Turkey-based engineering).