Operations Research & Optimization Engineer

Operations Research & Optimization Engineer

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Why This Role

Airline crew rostering is one of the most complex real-world optimization challenges, thousands of constraints, millions of possibilities, and zero room for error. We're not looking for someone to maintain spreadsheets or tune simple heuristics. We're building autonomous decision-making systems that blend classical optimization, AI, and domain intelligence to generate schedules that are safe, efficient, and disruption resilient.

You'll be the technical owner of our optimization engine, modelling constraints, building solvers, running large-scale experiments and ultimately shipping a system that an entire industry depends on.

Bonus:

What You Bring

• Bachelor's or master's degree in Operations Research, Computer Science, Industrial Engineering, Applied Mathematics, or equivalent practical experience
• 5+ years of experience building or operating large-scale optimization or scheduling systems
• Strong expertise in one or more optimization techniques:
• Mixed-Integer Programming (MIP)
• Constraint Programming (CP)
• Heuristics / Metaheuristics (GA, Tabu, Simulated Annealing, LNS, etc.)
• Column generation or decomposition methods
• Hands-on experience with optimization tools like Gurobi, CPLEX, OR-Tools, MiniZinc, Pyomo, or custom solvers
• Ability to model complex rule systems (legalities, constraints, fatigue rules, preferences, unions, etc.)
• Proficiency in Python (bonus: experience in C++ for performance-critical components)
• Familiarity with ML-assisted optimization is a plus (RL, heuristic learning, forecasting, or delay modelling)
• Experience designing experiments, tuning solvers, and interpreting model output
• Strong understanding of production engineering practices: CI/CD, version control, testing frameworks, monitoring
• Excellent communication and collaboration skills; ability to thrive in a fast-paced, problem-solving, startup environment

What You'll Do

• Design, build, and optimize the core scheduling engine for airline crew and cabin rosters from constraint modelling to solver implementation
• Build scalable, parameterized formulations for pairing, rostering, bidding, fatigue compliance, legality checks, and disruption recovery
• Develop and tune algorithms (MIP, CP, heuristics, or hybrids) capable of handling very large problem instances
• Create automated pipelines to run what-if scenarios, simulations, and optimization experiments
• Integrate real-world data sources schedules, crew profiles, aircraft rotations, rule sets into the optimization engine
• Collaborate closely with product, domain experts, and engineering teams to define rules, constraints, and objective functions
• Experiment quickly: prototype new formulations, prune search spaces, evaluate heuristics, and benchmark solver performance
• Work with AI/ML engineers to blend optimization with predictive modelling, reinforcement learning, or heuristic learning where beneficial
• Help define reliability, feasibility guarantees, and quality metrics for solver outputs
• Turn complex airline rules into simple, elegant models that consistently produce feasible and high-quality schedules
• Contribute to building the next generation of autonomous operational decision systems used inside enterprise environments

Benefits

• Competitive compensation
• Flexible PTO and work hours
• Opportunity to work with leading-edge AI tools and enterprise technologies
• High-impact role shaping the next generation of intelligent supply chain systems
• Research potential and university collaboration