Instacart
Engineering Manager, Machine Learning (Caper)
Remote Leadership (Engineering) role with clear candidate location fit.
PostedRecently added
Eligible countries1 accepted country
Seniority signalLead
Work settingRemote
Accepted candidate locations
USA
Role overview
Engineering Manager, Machine Learning (Caper)
Requirements and responsibilities
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About the Job
- Lead and grow a team of ~10 ML, CV 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.
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/CV/AI engineers, including hiring, performance management, and career development.
- Hands-on expertise with computer vision, 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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