Principal Computer Vision Engineer

We're transforming the grocery industry. At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.

• Architect and Own: Drive the evolution of our core computer vision pipelines, taking algorithms from initial research and training through to highly optimized production deployment.
• Blend Classic & AI: Design and implement robust classic computer vision algorithms alongside deep learning architectures to solve complex spatial, alignment, and tracking challenges.
• Model Development: Train, evaluate, and fine-tune state-of-the-art deep learning models for object detection, classification, and advanced segmentation.
• Optimize for Production: Build and optimize high-performance inference pipelines in Python and C++, leveraging optimization tools like TensorRT and CUDA to ensure low-latency execution.
• Technical Leadership: Drive technical excellence across the AI stack, evaluating new architectures, standardizing our training methodologies, and making pragmatic tradeoffs between accuracy, speed, and compute constraints.
• Deliver End-to-End: Handle technical design, clean architecture, implementation, observability, and iterative improvements of our perception engine.

• 6+ years of industry experience in computer vision, machine learning, or a related field.
• Deep expertise in classic computer vision techniques, including strong practical knowledge of optical flow, multi-view geometry, feature matching, and homography estimation (e.g., using OpenCV).
• Strong foundational understanding of modern deep learning architectures, specifically for object detection, image classification, and semantic/instance segmentation.
• Production-level programming skills in Python.
• Extensive hands-on experience with PyTorch and building scalable model training and evaluation pipelines.
• Proven track record of optimizing complex algorithms for deployment in resource-constrained or real-time environments.

• Hybrid (3 days/week in office required).