Network Optimization & AI/ML Engineer, IRIS2

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An ambitious role at SES for a (Senior) Engineer, Network Optimization & AI/ML, IRIS2, focusing on designing optimization models and AI-driven decision support for routing, traffic engineering, and capacity in a multi-orbit satellite network. The ideal candidate blends operations research, graph algorithms, ML, and network engineering to transform constraints into practical algorithms that improve latency, throughput, resilience, and efficiency. Key signals: strong optimization and graph theory background, hands-on ML plus Python and tooling (NumPy, SciPy, PyTorch, OR-Tools, Gurobi/CPLEX), SDN or telecom familiarity, and track record moving prototypes to production. Prepare to work 60% onsite, obtain SECRET clearance, and travel as needed; tailor your CV with concrete experiments, metrics, and deployment-ready components.

The job responsibilities outlined in this document are not exhaustive and may evolve over time and be reviewed according to business needs.

PROGRAMME DESCRIPTION

IRIS2 is the new European Union secure satellite constellation. This project is the European Union's answer to the pressing challenges of tomorrow to provide secure connectivity services and enhanced communication capacities to the EU and its Member States. In addition, as well as to governmental users, private companies and European citizens while benefiting from ensuring high-speed internet broadband to cope with connectivity dead zones.

SES – together with other consortium partners and core members, was selected by the European Commission to build and to operate the IRIS2 multi-orbit satellite constellation.

The IRIS2 team enters next project phases, we seek and there is a strong need of support for all the activities around service provisioning and products that the system can offer in the future.

ROLE DESCRIPTION SUMMARY

We are seeking a Network Optimization & AI/ML Engineer to develop optimization models, algorithms, and data-driven techniques for routing path selection, traffic engineering, capacity allocation, prediction, and autonomous network behavior in a multi-orbit satellite communications environment. This role sits at the intersection of operations research, graph algorithms, machine learning, AI-driven decision support, and network engineering. The position is ideal for someone who can transform complex network and constellation constraints into practical algorithms that improve latency, capacity utilization, resilience, and operational efficiency.

PRIMARY RESPONSIBILITIES / KEY RESULT AREAS

• Formulate optimization problems for routing path selection, traffic engineering, resource allocation, congestion mitigation, and service assurance.
• Develop algorithms for dynamic, multi-layer satellite-terrestrial network scenarios with changing topology, demand, failures, and operational constraints.
• Apply AI and machine learning techniques for traffic prediction, anomaly detection, failure mitigation, self-optimization, and decision support.
• Build prototypes and evaluation pipelines to compare optimization strategies against baseline routing and control-plane approaches.
• Work with architecture, simulation, control-plane, software, and validation teams to integrate algorithms into experimental and production-oriented workflows.
• Define objective functions, constraints, metrics, data requirements, and validation approaches for algorithmic network optimization.
• Analyze trade-offs across latency, throughput, availability, fairness, capacity, security, and operational complexity.
• Document mathematical models, assumptions, performance results, and implementation guidance for technical and programme stakeholders.

Qualifications Background and Experience

• Advanced degree in Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, Data Science, or a related field, or equivalent professional experience.
• Strong foundation in optimization, including convex optimization, linear programming, integer programming, combinatorial optimization, heuristics, and metaheuristics.
• Strong foundation in graph theory, network flow, routing algorithms, stochastic processes, queueing theory, and performance modeling.
• Hands-on experience with machine learning, time-series forecasting, anomaly detection, reinforcement learning, or AI-driven decision systems.
• Programming skills in Python and experience with scientific computing and optimization tooling such as NumPy, SciPy, PyTorch, TensorFlow, OR-Tools, Gurobi, CPLEX, or similar frameworks.
• Ability to design statistically rigorous experiments, evaluate algorithm performance, and communicate assumptions and limitations clearly.
• Familiarity with SDN, traffic engineering, satellite networks, telecom systems, or distributed network control is highly valuable.
• Experience moving research prototypes toward maintainable software components or operational decision-support tools.

OTHER KEY REQUIREMENTS / COMMENTS

• The candidate must be eligible for a “SECRET” security clearance, in accordance with the national regulations as well as EU/ESA/NATO equivalents
• Willing to work 60% onsite from office
• Travel as required for project realization purposes