Senior Algorithm / Optimization Engineer (AMR Fleet Scheduling & Traffic)
About MOV.AI
MOV.AI is a robotics software company developing an autonomy platform for the next generation of Autonomous Mobile Robots (AMRs), operating in large-scale industrial and logistics environments. The platform enables fleets of robots to navigate, localize, and operate reliably in complex real-world scenarios, ranging from international airports to major warehouse operations.
Role Summary
The company is looking for a Senior Algorithm / Optimization Engineer responsible for designing and implementing mathematical algorithms for:
- Transport order planning and assignment
- AMR scheduling
- Routing and traffic optimization
- Fleet management
The successful candidate will develop algorithms capable of generating efficient execution plans while considering multiple robots, operational constraints, priorities, and real-time conditions.
This is not a Data Science or research-focused position. The objective is to transform mathematical models into robust, production-grade software.
Candidate Profile
1. Mathematical Optimization Specialist
The ideal candidate should have a strong background in:
- Mathematical Optimization
- Operations Research
- Graph Algorithms
- Scheduling
- Routing
- Assignment Problems
- Heuristics
- Metaheuristics
The engineer should understand the trade-offs between:
- Solution quality
- Runtime performance
- Robustness
- Operational feasibility
Experience with multi-objective optimization is expected, considering factors such as:
- Throughput
- Travel distance
- Traffic congestion
- Battery status
- Resource utilization
- Fairness across robots
2. Logistics & Fleet Planning
Candidates should have experience with:
- AMR Fleet Management
- Intralogistics
- Transport Orders
- Pick-up & Drop-off operations
- Deadlines
- Priorities
- Route Conflicts
- Traffic Bottlenecks
- Charging Constraints
- Robot Availability
- Route Alternatives
The ability to translate complex logistics scenarios into executable optimization models is essential.
3. Software Engineering
The role requires building production-ready software rather than research prototypes.
Primary programming languages:
Additional experience should include:
- APIs
- Backend Services
- Data Structures
- Concurrency
- Performance Profiling
- Docker
- Kubernetes
- Git
- CI/CD
- Code Reviews
- Automated Testing
4. Data-Aware Algorithm Development
Experience using historical operational data to improve algorithms is highly valued, including:
- Machine Learning
- Statistical Models
- Travel Time Prediction
- Congestion Prediction
- Throughput Prediction
- Failure Risk Prediction
Candidates should also be comfortable validating algorithms through:
- Simulation
- Replay
- Back-testing
- Benchmarking
- Operational KPIs
Required Skills
Mathematics & Optimization
Strong knowledge of:
- Operations Research
- Combinatorial Optimization
- Scheduling
- Routing
- Graph Search
- Resource Allocation
- Constraint Modelling
- Objective Functions
- Dynamic Programming
- Greedy Algorithms
- Local Search
- Metaheuristics
- Runtime Complexity
Software Engineering
- Rust
- Python
- Clean Code
- Modular Design
- Testing
- APIs
- Backend Integration
- Git
- CI/CD
AMR Fleet Planning
Experience with:
- Fleet Optimization
- Multi-Robot Scheduling
- Traffic Management
- Route Planning
- Charging Constraints
- Robot Capabilities
- Operational Constraints
- Execution Planning
Performance & Validation
Knowledge of:
- Algorithm Benchmarking
- Performance Profiling
- Simulation
- Back-testing
- KPIs
- Throughput Analysis
- Fleet Utilization
- Traffic Congestion Analysis
- Plan Stability
Preferred Technologies
Optimization
- OR-Tools
- MILP Solvers
- CP-SAT
- Linear Programming
- Constraint Programming
Event-Driven Technologies
Cloud & Infrastructure
Robotics
Simulation
- Digital Twins
- Gazebo
- Operational Replay Systems
Machine Learning
- Reinforcement Learning
- Time-Series Forecasting
- Statistical Modelling
Key Responsibilities
The successful candidate will:
- Design and develop transport order assignment algorithms.
- Build scheduling algorithms.
- Develop routing and traffic optimization algorithms.
- Optimize AMR fleet performance.
- Model operational constraints.
- Generate real-time executable plans.
- Continuously improve algorithms using historical operational data.
- Build simulation and benchmarking environments.
- Integrate optimization algorithms into backend services.
- Define KPIs and performance metrics to evaluate algorithm effectiveness.
Deal Breakers
Candidates will not be considered if they:
- Lack a solid mathematical optimization background.
- Have only Machine Learning experience without optimization expertise.
- Have only academic or research experience.
- Cannot develop production-grade software.
- Lack experience with scheduling, routing, or resource allocation problems.
- Are unable to collaborate with backend engineering teams.
- Have no interest in robotics, logistics, or industrial software.
Compensation
- Gross annual salary: €60,000 to €80,000
- Paid over: 14 monthly salaries.
