Senior Algorithm / Optimization Engineer – AMR Fleet Scheduling & Traffic

Location: Tagus Park, Portugal

Work Model: Hybrid - 3 days on-site

Contract: Full-time

Salary: €60,000-€80,000 gross/year × 14 salaries

Relocation: No relocation support provided

About the Role

We are looking for a Senior Algorithm / Optimization Engineer to design and implement advanced algorithms for Autonomous Mobile Robot (AMR) fleet scheduling, routing, order assignment, and traffic optimization.

This is a hands-on engineering role combining mathematical optimization, logistics planning, and production-grade software development.

What You'll Do

- Design algorithms for AMR assignment, scheduling, routing, and fleet optimization

- Model orders, priorities, deadlines, routes, robot capabilities, and operational constraints

- Develop fast, executable planning solutions balancing optimality and real-time performance

- Work with historical and live data to improve travel-time estimates and planning quality

- Build simulation, benchmarking, and validation tools

- Integrate algorithms into production backend and fleet-management systems

- Collaborate with Backend, Robotics, Product, QA, Simulation, and Fleet Management teams

Requirements

- Strong background in mathematical optimization, algorithms, Operations Research, or applied mathematics

- Experience with combinatorial optimization, scheduling, routing, assignment, or resource allocation

- Strong programming skills in Rust and/or Python

- Production software engineering experience - not only research or prototypes

- Understanding of algorithms, constraints, objective functions, heuristics, and runtime complexity

- Experience with APIs, backend services, testing, Git, CI/CD, and performance optimization

- Ability to evaluate algorithms using simulation, benchmarks, data, and operational KPIs

- Understanding of logistics, fleet planning, or complex scheduling problems

Nice to Have

- AMR/AGV, robotics, warehouse automation, or intralogistics experience

- OR-Tools, MILP, CP-SAT, constraint programming, or similar

- Vehicle routing, job-shop scheduling, pathfinding, or traffic management

- Machine learning/statistical modelling applied to operational problems

- Docker/Kubernetes

- ROS, VDA 5050, MQTT, Kafka, Redis, or similar technologies

- Simulation/digital twin experience

Important

This is not a pure Data Science or theoretical research role.

Candidates must be able to translate mathematical and optimization concepts into robust, maintainable, production-ready algorithms used in real-world fleet operations.

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