Senior Algorithm / Optimization Engineer – AMR Fleet Scheduling & Traffic

Velenosi&Meredith; Porto; Hybrid; EUR 60,000 - 80,000; Full time

Job summary

Velenosi&Meredith is seeking a Senior Algorithm / Optimization Engineer in Porto to design and implement advanced optimization algorithms for Autonomous Mobile Robot (AMR) fleet scheduling, routing, and traffic management. The role blends mathematical optimization with production-grade software development.

You will model orders, priorities, routes, and robot capabilities, and develop fast planning solutions that balance optimality with real-time performance, integrating with production backend

Qualifications

• 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.

Responsibilities

• 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.

Skills

Mathematical optimization

Operations Research

Applied mathematics

Combinatorial optimization

Scheduling

Routing

Assignment

Resource allocation

Algorithms

Runtime complexity

Tools

OR-Tools

MILP

CP-SAT

Constraint programming

Docker/Kubernetes

ROS

Job description

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.

LinkedIn: https://www.linkedin.com/in/dylan-デ-a78965140/