Senior Machine Learning Engineer II, Fulfillment, Matching and Positioning

Instacart is transforming the grocery industry and is looking for a Senior Machine Learning Engineer to join their Matching & Positioning team. In this role, you will design and ship algorithms that impact profitability and customer satisfaction while collaborating with various teams to solve complex problems.

Responsibilities:

• Design, implement, and deploy algorithms for order batching, real-time shopper assignment, routing, and marketplace positioning using techniques such as MIP/CP-SAT, heuristics/metaheuristics, and learning-to-rank
• Own the full model lifecycle: problem formulation, data pipelines and features, offline evaluation and simulation, A/B testing, staged rollouts, and ongoing monitoring/observability
• Build reliable, low-latency services in Python (and, where performance dictates, C++ or Go) that integrate with solvers (e.g., OR-Tools, Gurobi, CPLEX) and run on cloud infrastructure with Docker/Kubernetes
• Partner with product, operations, and data science to define roadmaps and success metrics; deliver measurable impact to on-time rates, shopper utilization, cost per order, and customer experience
• Leverage experimentation and causal methods along with offline counterfactual replay/simulation to validate changes and de-risk launches
• Contribute to engineering excellence through code reviews, design docs, robust testing, and participation in an on-call rotation for mission-critical fulfillment services; mentor peers and raise the technical bar

Requirements:

• Bachelor's degree in Computer Science, Operations Research, Electrical Engineering, Applied Mathematics, or a related field (or equivalent practical experience)
• 5+ years of professional experience building and shipping ML and/or optimization systems to production
• 3+ years formulating and solving large-scale combinatorial optimization problems (e.g., VRP, matching, scheduling) using solvers such as OR-Tools, Gurobi, or CPLEX (MIP/CP-SAT) and heuristic methods
• Proficiency in Python and SQL, including writing production-quality code with testing, profiling, and code review practices
• Hands-on experience deploying algorithms/models as microservices with Docker and Kubernetes on a major cloud provider (GCP or AWS), including monitoring, alerting, and dashboards
• Experience designing and operating low-latency decision services in high-throughput environments (targeting sub-second P95 response times)
• Practical experience with A/B testing or online experimentation platforms, from hypothesis through analysis and rollout decisions
• Strong collaboration and communication skills with engineering, product, and data science stakeholders
• Master's or PhD in Operations Research, Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field
• Domain experience in logistics, ride-hailing, delivery, or marketplace optimization at scale
• Familiarity with reinforcement learning or contextual bandits for online decision-making and exploration/exploitation tradeoffs
• Experience with geospatial data, routing APIs, and graph algorithms
• Background in building simulation frameworks and counterfactual evaluation for decision systems
• Experience with streaming data and real-time feature computation (e.g., Kafka, Flink) and feature stores
• Proficiency in C++ or Go for performance-critical components
• Track record of mentoring engineers and leading cross-functional projects to measurable outcomes
• Experience participating in an on-call rotation for production ML/optimization services