About Pelico

We are creating the factory of the future where disruptions are avoided, processes synchronized and value captured. Our factory operations management platform enables factory teams to be more agile and resilient in a world where supply chain disruptions occur every 16 minutes. Pelico empowers users to identify bottlenecks, avoid problems, and focus on innovation instead of fire-fighting. Since our foundation in 2019, we’ve partnered with industry leaders across aerospace, industrial equipment, and luxury watchmaking, revolutionizing factory operations in over 15 countries. Esteemed clients include Airbus, Safran, Cartier, Daikin, and Eaton.

Our Team

With a dynamic team of over 180 professionals across the US and France, Pelico is a melting pot of top-tier talent from Tech, Data Science, and Manufacturing domains.
Our collaborative environment fosters innovation and excellence, driving us to solve complex challenges and shape the future of manufacturing.

Our work has been recognized by Safran (Digital Transformation Award) and Microsoft (scale-up of the year).

Engineering turns Product ambition into scalable, reliable solutions that empower on-site operational teams.

We design a platform that handles high volumes, concurrent users, and complex workflows, while remaining modular, maintainable, and future-proof.

About the Role

We are hiring a Head of Optimization to found and lead Solver as a player-coach. Your mandate is concrete: forecast missing parts before they bite, explore and simulate mitigations, and present production controllers with defensible trade-offs — then make all of it actionable by autonomous AI agents so Pelico can build a semi-autonomous production-control copilot. You will start with a tight founding group (3–4 engineers, including two of Pelico's deepest planning-algorithm engineers) and grow it only as the business impact you create justifies it. This is a hands-on role first and a leadership role second — at this stage they are the same job.

What You Will Do

1. Forecast, Simulate, and Optimize (hands-on)

• Personally formulate and ship production optimization on Pelico's discrete-manufacturing domain: multi-level pegging/BoM, coverage, shortage prediction, capacity and allocation — using MILP/CP, metaheuristics, and discrete-event/what-if simulation as the problem demands

• Build the forecast-missing-parts → explore-mitigations → simulate-impact → present-trade-offs loop as a production capability, not a notebook

• Frame mitigation as multi-objective decision support (recovery speed vs. cost vs. customer OTD), surfacing a small set of defensible options to controllers

2. Industrialize Algorithm Families (precision ↔ speed ↔ memory)

• Treat each problem as an algorithm family: explore, evaluate, and ship multiple variants trading precision against speed and memory (exact for small/critical instances; LNS/metaheuristic for mid-size; anytime/greedy for interactive copilot latency)

• Stand up a benchmark/eval harness (golden instances; regression on solution quality, latency, and memory; instance-feature coverage) as a first-class, compounding asset — and guard against portfolio overfitting

• Win performance at hundred-million-record scale with the founding engineers — allocation-aware, in-memory, parallel — because at Pelico the systems engineering and the math are inseparable

3. Make Optimization Agent-Actionable

• Package optimization as governed, typed, explainable, eval'd tools the production-control copilot can invoke under human-in-the-loop guardrails — the value is only realized when an agent can call it safely

• Partner with the ADK and Co-pilot squads on the tool contracts, review gates, and eval that let agents close the loop from detection to proposed action

• Define the correctness, stability, and explainability bar for optimization that an autonomous agent — and a production controller — can trust

4. Lead the Pairing and Found the Team — Impact-Gated

• Run Solver's load-bearing OR ↔ Backend-SWE pairing: author models and specs the founding engineers implement and scale, so correctness lives upstream of code and nothing collapses into bespoke per-customer forks

• Set the bar by example — write models, write code, review PRs at depth — and mentor the founding engineers (deep Kotlin planning-algorithm experts) into optimization technique

• Earn headcount by shipping impact, then hire deliberately against business-impact opportunities

What You Bring

Required

• Advanced degree in Operations Research, Applied Mathematics, or Industrial Engineering — or a track record that unambiguously demonstrates equivalent depth

• Proven production work across combinatorial optimization (MILP/LP/CP), metaheuristics, and simulation — shipped on commercial/open solvers (Gurobi / CPLEX / OR-Tools / Hexaly or equivalent), solving real problems

• Discrete-manufacturing or supply-chain planning depth: MRP, multi-level BoM, pegging, coverage, scheduling, allocation, or inventory under uncertainty

• Experience making the exact-vs-heuristic, precision-vs-speed-vs-memory trade-off deliberately — and evaluating algorithm variants rigorously

• Large-scale performance instinct: optimization or simulation on millions-to-hundreds-of-millions of records, in-memory, allocation- and latency-aware

• Hands-on coding in a production codebase, shipping and reviewing daily (the squad builds in Kotlin/Java; fluency or a credible fast ramp)

• Demonstrated lead-by-example leadership — you have led by being the best engineer in the room, not by leaving the keyboard

Strongly Preferred

• Aerospace & defense or discrete-manufacturing domain (pegging, coverage, capacity planning)

• Built optimization or simulation packaged for programmatic/agent consumption (typed APIs, eval harnesses, anytime profiles)

• Algorithm-portfolio / automated-algorithm-selection experience

• AI-native delivery: agentic workflows, eval on model outputs, spec-driven development

• French language fluency (HQ benefit, not a requirement)

Why This Role Matters

Forecasting missing parts, simulating the fixes, and handing controllers trustworthy trade-offs — then letting an agent act on them — is the capability that turns Pelico from a visibility tool into a semi-autonomous production-control copilot. It is the math competitors cannot easily copy, and it is a direct lever on win rates, expansion, and pricing power in aerospace and defense. As the player-coach who both invents the optimization and leads the team that ships it at scale, the Head of Optimization turns Pelico's hardest technical bets into a durable competitive moat.