ML Research Engineer – Power Systems

EnliteAI

. Develop machine learning and reinforcement learning methods for grid operation problems .

Posted 9/21/2026full-timeVienna • AustriaMid-LevelSenior💰 €70,000 - €90,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning and reinforcement learning, with a strong focus on developing and validating models for grid operation problems. Proficient in Python and PyTorch, with a solid understanding of experimental design and the full development cycle from prototype to production.

Highest-signal resume keywords

Machine Learning ExpertiseReinforcement Learning ExpertiseStrong PyTorch SkillsPython ProgrammingExperimental Design Rigour

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningReinforcement LearningState EstimatorsSurrogate ModelsGraph Neural NetworksForecasting MethodsData PipelinesModel ValidationMVP DevelopmentSoftware Engineering

Soft Skills

Strong Communication SkillsSelf-Directed ApproachCollaborative MindsetLow-Ego Attitude

Tools & Technologies

PandapowerPyPSAOpenDSSGrid2Op

Industry Keywords

Energy InformaticsElectrical EngineeringApplied MathematicsResearch PublicationsEU-Funded Projects

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Develop machine learning and reinforcement learning methods for grid operation problems
Build learned and hybrid state estimators, surrogate models, graph neural networks, forecasting methods and reinforcement learning controls
Review and assess state-of-the-art research for industrial-scale implementation
Design and run experiments with baselines, ablations and realistic data regimes
Validate methods against pandapower, PyPSA, OpenDSS and Grid2Op
Contribute to core Maze components and extend the framework for energy and infrastructure applications
Own model-facing data pipelines, including domain transforms, unit and sign conventions, features, train/serve consistency and schema contracts
Take prototypes through MVP and production transition
Contribute to deliverables and publications in EU-funded research projects

Requirements

What you’ll need

Fluent English with strong communication skills
Deep, hands-on machine learning and reinforcement learning expertise
Strong PyTorch skills
Strong Python and sound software engineering practice
Ability to read and reproduce research papers
Rigour in experimental design, including meaningful baselines and controlled comparisons
Self-directed approach
Low-ego and collaborative mindset
Willingness to own the full development cycle through deployed MVP
A degree in computer science, energy informatics, electrical engineering, applied mathematics or a related field, or equivalent practical experience
Valid work permit for Austria

Benefits

Comp & perks

Research problems that are genuinely open, on infrastructure that matters
EU Horizon projects with opportunity to publish
Publicly visible open-source work through Maze
Dedicated GPU capacity on the company’s own compute cluster
Hybrid working with minimal core hours
Dedicated time and budget for R&D, conferences and professional development