Operations Research & Optimization Engineer

**Job Description:** As an Operations Research & Optimization Engineer, you will be tackling one of the most complex real-world optimization challenges in the airline industry - crew rostering. This role goes beyond maintaining spreadsheets or simple heuristics; you will be instrumental in building autonomous decision-making systems that combine classical optimization, AI, and domain intelligence to create schedules that prioritize safety, efficiency, and resilience to disruptions. **Key Responsibilities:** - Design, build, and optimize the core scheduling engine for airline crew and cabin rosters, covering constraint modeling to solver implementation. - Develop scalable and parameterized formulations for various aspects such as pairing, rostering, bidding, fatigue compliance, legality checks, and disruption recovery. - Implement and fine-tune algorithms including Mixed-Integer Programming (MIP), Constraint Programming (CP), heuristics/metaheuristics (GA, Tabu, Simulated Annealing, LNS, etc.), and column generation methods. - Create automated pipelines for running simulations, what-if scenarios, and optimization experiments. - Collaborate with product, domain experts, and engineering teams to define rules, constraints, and objective functions. - Prototype new formulations, evaluate heuristics, and benchmark solver performance to continuously improve the scheduling engine. - Work closely with AI/ML engineers to integrate optimization with predictive modeling, reinforcement learning, or heuristic learning. - Translate complex airline rules into elegant models that consistently generate feasible and high-quality schedules. - Contribute to the development of autonomous operational decision systems used in enterprise environments. **Qualifications Required:** - Bachelor's or master's degree in Operations Research, Computer Science, Industrial Engineering, Applied Mathematics, or related field. - 5+ years of experience in building or operating large-scale optimization or scheduling systems. - Strong expertise in optimization techniques such as MIP, CP, heuristics/metaheuristics, and column generation methods. - Hands-on experience with optimization tools like Gurobi, CPLEX, OR-Tools, MiniZinc, Pyomo, or custom solvers. - Proficiency in Python; experience in C++ is a bonus for performance-critical components. - Familiarity with ML-assisted optimization, experimentation design, and interpreting model output. - Understanding of production engineering practices including CI/CD, version control, and testing frameworks. - Excellent communication and collaboration skills to thrive in a fast-paced, problem-solving startup environment. (Note: Additional details about the company were not provided in the job description.),