Senior Optimization Engineer
LegionHybrid, Bucharest, RomaniaPosted 21 March 2026
Job Description
Senior Optimization Engineer
Hybrid, Bucharest, Romania
JOB OVERVIEW
As a Senior Optimization Engineer on the Schedule Optimization team, you’ll play a key role in building and scaling Legion’s intelligent scheduling that implements sophisticated scheduling and optimization algorithms. You will use your expertise in linear algebra, optimization modeling (including MILP/LP), and production-grade Java systems to deliver solutions that balance business requirements, compliance rules, and employee preferences at scale.
In this role, you’ll be responsible for designing, implementing, and productionizing optimization services and integrating them into Legion’s backend architecture. You’ll help shape how millions of hourly workers get scheduled more fairly and efficiently, thereby helping realize Legion’s mission.
RESPONSIBILITIES AND DUTIES
Design, develop, and optimize scalable optimization algorithms for workforce scheduling and labor planning.
Formulate and implement Mixed Integer Linear Programming (MILP) and Linear Programming (LP) models using Gurobi (or equivalent solver).
Translate complex business rules (labor laws, staffing constraints, employee skills/preferences) into mathematical formulations and solve them in production.
Write high-quality, secure, efficient enterprise-grade Java code, integrated with the backend stack (Spring Boot, Hibernate, MySQL).
Optimize runtime performance, throughput, and scalability of optimization services to support large enterprises.
Integrate optimization services into APIs and production systems, working cross-functionally with Data Science, Product, and Platform Engineering teams.
Participate in on-call rotations and monitor/maintain production systems for reliability and performance.
Mentor junior engineers and contribute to best practices in optimization modeling, software design, and operations.
REQUIRED SKILLS AND QUALIFICATIONS
Master’s degree or PhD in Applied Mathematics, Operations Research, or a related quantitative field.
5–8 years of professional software engineering experience, with strong Java development expertise.
Solid understanding of mathematical optimization concepts including linear programming and mixed integer programming.
5+ years of experience writing enterprise-quality Java code, including secure, efficient, scalable implementations.
Strong understanding of distributed systems, APIs, data processing, and performance/scalability tradeoffs.
Excellent communication skills and ability to collaborate across teams and mentor others.
PREFERRED QUALIFICATIONS AND SKILLS
Experience with Gurobi or equivalent optimization solvers (CPLEX, OR-Tools, SCIP).
Experience in workforce scheduling, shift optimization, traffic optimization or other labor/resource scheduling domains.
Familiarity with constraint programming (CP), metaheuristics, or stochastic optimization.
Ability to operate in fast-paced, resource-constrained environments.
ABOUT LEGION
Join Legion's mission to turn hourly jobs into good jobs. We're a mission-driven team seeking exceptional talent to propel this vision. Embrace a culture that's collaborative, fast-paced, and entrepreneurial. With us, you'll grow your skills, work closely with experienced executives, and contribute significantly to our mission. Our award-winning AI-native workforce management platform is intelligent, automated, and employee-centric and proven to deliver 13x ROI. We help labor-intensive organizations maximize labor efficiency and employee engagement simultaneously. Legion has earned recognition for its innovation, including spots on the Inc. 5000 list, Forbes’ Next Billion Dollar Startups, and awards for our AI technology. Backed by leading investors such as Norwest Venture Partners, Stripes, First Round Capital, XYZ Ventures, Webb Investment Network, Workday Ventures, and NTT DOCOMO Ventures, we're making real change.
If you're ready to make an impact and grow your career, Legion is where you belong. Join ... (truncated, view full listing at source)
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