Engineering Manager, Machine Learning (Caper)

Instacart

11h • Remote

About The Position

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.

Requirements

• 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.

Nice To Haves

• Experience integrating on-device ML with Android applications and collaborating closely with Android teams on SDKs and APIs.
• Background with multimodal vision-language models (VLMs) and large language models (LLMs) for perception, retrieval, or instruction-based reasoning.
• Experience with sensors and hardware integration (e.g., multi-camera setups, weight sensors), calibration, and dataset generation for robotics or retail environments.
• Demonstrated success leading cross-functional programs across 3+ partner teams and delivering multi-quarter roadmaps.
• Graduate degree (MS/PhD) in a relevant field with research or applied focus in computer vision, machine learning, or robotics.

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Number of Employees: 501-1,000 employees