Senior Technical Program Manager, Instacart

• This role is ideal for a builder who thrives in a fast-paced, evolving environment and enjoys rolling up their sleeves to turn complex, cross-functional work into predictable delivery
• 6+ years of technical program management experience, ideally leading end-to-end ML/AI programs from data to production
• 3+ years working on computer vision or edge/on-device ML for embedded, mobile, or hardware-integrated products
• Proven track record delivering 3+ production ML model releases or ML-powered features with measurable impact on product metrics
• Bachelor’s degree in Computer Science, Electrical/Computer Engineering, or a related technical field; or equivalent practical experience
• Hands-on familiarity with the ML lifecycle, including data pipelines, labeling/annotation workflows, model training/evaluation, and MLOps/CI-CD for ML
• Ability to query and analyze data using SQL and basic Python to validate assumptions and interpret model and product performance
• Experience coordinating cross-functional teams of 10+ stakeholders, including external vendors, with strong risk, dependency, and change management
• Excellent written and verbal communication skills, including executive-level reporting and decision facilitation
• Willingness to travel to stores, labs, and partner sites as needed (up to 15%)

Desirable

• Caper, an Instacart team building AI-powered checkout experiences, is on the cutting edge of physical AI—bringing computer vision and on-device machine learning to the real world through smart carts and connected retail hardware
• We are seeking a Senior Technical Program Manager to lead complex, multi-quarter machine learning & computer vision engineering programs that power the heart of Caper: high-quality data pipelines, robust model training and evaluation, and reliable on-cart deployment at scale
• You will partner closely with computer vision and machine learning engineers, Android engineers, hardware teams, product, operations, and the executive team to build the roadmap, drive execution, and deliver measurable improvements to model performance and in-store outcomes
• Own end-to-end execution of ML programs across data collection, labeling, training, evaluation, and on-device deployment—defining scope, milestones, risks, and success metrics tied to accuracy, latency, and in-store experience
• Build and maintain cross-functional plans with computer vision/ML, Android, hardware, MLOps, data engineering, QA, and field operations; run cadences, surface tradeoffs, and drive on-time, high-quality releases
• Lead data operations at scale, including in-store capture, synthetic generation, and third-party annotation—setting data quality bars, SLAs, and privacy standards in partnership with Legal and Security
• Establish rigorous evaluation frameworks that connect offline metrics and on-device performance to business outcomes (e.g., recognition accuracy, shrink reduction, throughput), including pilots and A/B tests in stores
• Drive deployment readiness and reliability: model packaging for edge devices, rollout plans, monitoring and alerting, incident response, and postmortems with clear, actionable follow-ups
• Communicate program status and insights to stakeholders and executives; proactively manage dependencies and