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. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview Caper Carts are AI-powered, intelligent shopping carts developed by Instacart that let customers scan, weigh, and pay for items directly on the cart—eliminating checkout lines. Equipped with cameras and sensors, these carts automatically recognize items, offer personalized promotions, and feature a touchscreen for real-time, interactive shopping. This machine learning team builds the brain behind the cart. We're hiring an Engineering Manager, Machine Learning to lead a team of talented ML and AI infrastructure engineers who power perception, multimodal understanding, and edge inference for Caper Carts. You will own the roadmap for how our carts see and reason about what's in the basket, and you'll build the platforms and models that make checkout seamless in dynamic, real-world retail environments. Your direct team will be ~10 engineers within a broader organization of ~30 spanning Android and hardware. This is a high-impact role at the frontier of physical AI—bridging edge devices in stores with cloud-scale data and training systems. You'll partner closely with Android, hardware, product, and operations to deliver measurable improvements in recognition accuracy, latency, and reliability. The role is remote across Canada; West Coast time zones are ideal, but we're open to great talent anywhere in the country. Learn more about our work at Connecting stores from edge to cloud: reinventing retail with physical AI. About the Job Lead and grow a team of ~10 ML and AI infrastructure engineers building the perception and reasoning systems that power Caper Carts in live retail environments. Define the technical vision, roadmap, and success metrics for cart perception and multimodal understanding; prioritize work that drives measurable gains in item recognition accuracy, checkout speed, and system reliability. Architect scalable training, data, and inference platforms on GCP using Ray, Kubernetes, and modern MLOps practices to enable rapid experimentation and safe, repeatable deployments. Deliver production-grade CV/VLM models for multi-camera item detection, weighing, and basket reasoning; optimize on-device inference for low-latency, high-availability operation at the edge. Build the data flywheel end-to-end—instrumentation, labeling, evaluation, offline/online testing, and monitoring—to continuously improve performance across diverse store conditions. Collaborate cross-functionally with Android, hardware, product, design, operations, and retailer partners; communicate risks, tradeoffs, and timelines clearly in a fast-paced, ever-evolving environment. About You Minimum Qualifications 8+ years of experience building and deploying machine learning systems, with a strong focus on computer vision in production environments. 2+ years of experience managing teams of 6+ ML/AI engineers, including hiring, performance management, and career development. Hands-on expertise with deep learning (e.g., PyTorch), model training/evaluation, and MLOps practices for reliable CI/CD of ML services. Proven experience architecting and operating ML infrastructure on GCP (e.g., GKE, Vertex AI, BigQuery) and distributed training/inference with Ray; containerization with Docker and orchestration with Kubernetes. Experience delivering real-time edge inference, including model optimization (e.g., TensorRT, ONNX, quantization) and monitoring for latency, throughput, and accuracy. Proficiency in Python and SQL, with a track record of shipping end-to-end CV systems including data pipelines, experimentation, deployment, and post-launch iteration. Bachelor's degree in Computer Science, Electrical/Computer Engineering, or a related technical field, or equivalent practical experience. Preferred Qualifications Experience integrating on-device ML with Android applications and collaborating closely with Android teams on SDKs an