OR Scientist / Algorithms Engineer

About the Role

As an OR Scientist / Algorithms Engineer, you'll turn ambiguous, messy supply chain problems — inventory, network design, routing, planning, capacity — into correct, well-reasoned optimization models, and ship them as production-grade software. You'll work across the full arc from problem formulation through solver implementation to communicating impact, and you'll use (and help shape how the team uses) AI/LLM tooling as both a build accelerator and a product building block.

This is an entry/early-career IC role on our ladder: you'll own well-scoped pieces of a bigger problem under the guidance of senior scientists, with a clear growth path to Senior, Lead, and Principal as your scope and independence grow.

What You'll Do

• Formulate real supply chain decisions — inventory, routing, network design, planning, capacity — as LP/MIP, network flow, heuristic, or simulation models, stating and defending your assumptions.
• Diagnose and improve slow or infeasible optimization models: tighten formulations, apply valid inequalities, warm starts, decomposition, or heuristics as the situation calls for.
• Write clean, tested, production-grade Python (or the team's language) against solvers and modeling layers (e.g. Gurobi, CPLEX, OR-Tools via Pyomo/JuMP), and take models from prototype toward reliable, monitored production use.
• Use AI/LLM and agentic tooling critically — as an accelerator you validate line-by-line, and, where relevant, as a component you help design, evaluate, and guardrail inside an algorithmic pipeline.
• Connect every model back to the business decision it serves — service levels, lead times, plan stability — and recognize when an “optimal” plan is operationally useless.
• Explain your formulation, trade-offs, and results clearly to both technical and non-technical audiences, and take feedback as input rather than verdict.

What We're Looking For

Required

• Hands-on experience formulating and solving real optimization problems — LP/MIP, heuristics, network flows, or simulation — beyond coursework alone.
• Professional software engineering practice in Python (or a comparable language): version control, testing, and code review, not just notebooks.
• Exposure to at least one commercial or open-source solver via a modeling layer (e.g. Gurobi, CPLEX, OR-Tools, Pyomo, JuMP) — or clear evidence you pick this up fast.
• A critical, hands-on posture toward AI/LLM coding tools: comfortable using them, and equally comfortable explaining where they've gone wrong.

Preferred

• Prior exposure to supply chain or a comparable operational-decision domain (inventory, transportation, planning, network design).
• A track record of work that ran at real scale — large problem instances, meaningful SKU/location/order volumes, live production traffic — rather than only academic or proof-of-concept work.
• Experience shipping something that stayed in production and that others depended on, including monitoring and iteration, not just a handed-off prototype.
• A self-initiated project or improvement you drove end-to-end without being asked — we place a lot of weight on this kind of ownership.
• A novel formulation, technique, framework, paper, patent, or open-source contribution that measurably beat the prior approach.
• Background at a company or lab doing optimization, supply chain, or algorithmic decisioning at scale (logistics, e-commerce, ride-hailing, marketplaces, manufacturing, OR-heavy tech, or a strong research group).