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 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.

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Overview

Carrot Ads is Instacart's publisher advertising platform — powering ad serving, monetization, and measurement for enterprise retail partners. It's one of Instacart's most strategically important and fastest-growing businesses, and we're at an inflection point: moving from early externalization to a platform that meets the expectations of the world's most sophisticated retailers. To get there, we've formed a dedicated Carrot Ads engineering team, purpose-built to own that maturation end to end — working closely with product, enterprise solutions, and horizontal engineering teams across Serving, Measurement, Ads Quality, and Ads Manager.

We are seeking a Senior Engineering Manager to lead this team of 10+ engineers. You'll drive platform stability and reliability, own partner onboarding and escalations, and lead forward-looking product development — evaluating the technical capabilities of competing platforms, identifying what to build next, and prototyping new capabilities. In close partnership with Product, Sales, and enterprise partners, you'll shape what Carrot Ads becomes and channel longer-term investments into the broader Ads engineering roadmap.

About the Job

• Lead, coach, and grow a team of 10–12 software engineers; set clear goals, develop talent, and foster a culture of ownership, accountability, and continuous improvement.
• Own platform reliability and scalability end-to-end; define and meet SLAs/SLOs, improve observability, reduce incident frequency and time-to-recovery, and raise the overall bar for quality.
• Oversee enterprise partner integrations and launch readiness; drive efficient onboarding, manage technical escalations, and ensure smooth operations across multiple high-stakes stakeholders.
• Shape strategy and roadmap by synthesizing customer feedback, market and competitive insights, and internal priorities; translate long-term bets into executable plans across Serving, Measurement, Quality, and Ads Manager teams.
• Elevate engineering excellence with robust testing, CI/CD, performance optimization, and data-driven decision-making; deliver measurable business impact on a predictable cadence in a fast-moving environment.
• Invest in self-service platform capabilities and tooling that reduce operational burden, improve partner autonomy, and unlock efficient scale.

About You

Minimum Qualifications

• 8+ years of software engineering experience, including 4+ years managing engineering teams delivering platform or infrastructure products in a B2B or enterprise SaaS environment.
• Experience leading teams of 8+ engineers, including hiring, performance management, and career development.
• Demonstrated success operating in ambiguous, high-growth settings while balancing reactive partner needs with long-term platform investments.
• Proven cross-functional leadership driving outcomes across multiple engineering teams and stakeholders to deliver complex, multi-quarter roadmaps.
• Direct domain experience in ad tech, retail media, or publisher-facing platforms.
• Technical proficiency with distributed systems and cloud infrastructure (e.g., AWS or GCP), along with data and ads-adjacent technologies (e.g., event streaming, experimentation, measurement).
• Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience.

Preferred Qualifications

• Experience scaling enterprise integrations and managing partner-facing technical relationships and SLAs.
• Track record of building self-service platform capabilities and tooling that significantly reduce manual operations.
• Comfort applying market and competitive analysis to inform engineering strategy and investment decisions.
• Background with advertising serving, measurement/attribution, or campaign management systems.
• Experience establishing reliability practices at scale (e.g., SLO frameworks, incident management, on-call health, post-incident learning loops).

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