Instacart
Senior
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?
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