About the Role
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.
Key Responsibilities
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.
What We’re Looking For
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).
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