Design real-time optimization and ML systems for order batching, shopper routing, and marketplace assignment at Instacart

Instacart's Matching & Positioning team is hiring a Senior ML Engineer to build production-grade optimization and ML systems for order batching, shopper assignment, and routing under sub-second latency at high throughput. The role sits at the intersection of operations research, combinatorial optimization (MIP/CP-SAT, solvers like OR-Tools/Gurobi/CPLEX), and machine learning, owning the full model lifecycle from formulation to production monitoring.

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What this interview tests

Combinatorial optimization (MIP/CP-SAT, OR-Tools/Gurobi/CPLEX)
Real-time low-latency decision services at scale
Full model lifecycle: offline eval, A/B testing, staged rollout
Production ML infrastructure (Docker/Kubernetes, monitoring)
Marketplace/logistics domain tradeoffs (batching, routing, assignment)

Common question themes

Walk me through a large-scale combinatorial optimization problem you formulated and the solver/heuristic tradeoffs you made

How did you architect a decision service to hit sub-second P95 latency under high throughput?

Describe your process for taking a model from offline simulation through A/B testing to production rollout

How have you used counterfactual replay or simulation to validate a change before launch?

Tell me about a time you chose a heuristic over an exact solver — what drove that decision?

How candidates describe it

Real Senior Machine Learning Engineer II interview stories — retold from candidates' public write-ups, with sources.

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