Careers at EnliteAI Energy

We are looking for an ML Research Engineer to develop the methods behind our energy applications, and to carry them from the literature to something a network operator can rely on.

Distribution grids are increasingly operated close to their technical limits, while many operators lack full visibility of their own low-voltage networks. That combination produces a class of problems well suited to machine learning: inferring system state from sparse and imperfect measurements, forecasting under uncertainty, evaluating network constraints fast enough to act on them, and coordinating flexible resources at the grid edge. Current projects include state estimation and dynamic operating envelopes, and the emphasis will move as new work comes in.

What stays constant is the character of the problem. These are open questions at industrial scale rather than solved ones, the data is real measurement data with everything that implies, the physical constraints cannot be negotiated with, and the decisions have to be made in operational time.

We build and deploy these systems ourselves, so the methods you design end up running against real data, and you own that transition. Roughly half the role is research — literature, method design, and experiments that establish whether something actually works. The other half is engineering it into a system that survives production. Both halves are the job.

Alongside this you will contribute to Maze, extending it toward energy and infrastructure applications. That work is publicly visible and used beyond enliteAI.

You would join a team that combines reinforcement learning and optimization, data and platform engineering, and power systems expertise. That mix is deliberate: the physical plausibility of a result is something you can check with a colleague rather than guess at, and the infrastructure your experiments and deployments run on is owned by the team rather than left to you.

The domain itself is learnable and we will teach it — we would rather appoint an excellent ML engineer and provide the power systems knowledge than proceed the other way around. Expect three to six months to working competence. "Research" in the title describes the nature of the work rather than a seniority bar: this suits someone completing or recently completed a doctorate, as well as anyone who has built comparable research experience by another route.

• Develop machine learning and reinforcement learning methods for grid operation problems: learned and hybrid state estimators, surrogate models for fast constraint evaluation, graph neural networks that exploit network topology, forecasting under uncertainty, and reinforcement learning for control within operational limits
• Review and assess the state of the art, judging which published approaches are implementable at industrial scale and which remain limited to small test cases
• Design and run experiments with meaningful baselines, ablations and realistic data regimes, validated against established simulation environments (pandapower, PyPSA, OpenDSS, Grid2Op)
• Contribute to core components of Maze and extend it toward energy and infrastructure applications
• Own the model-facing side of our data pipelines: domain transforms, unit and sign conventions, feature definitions, train/serve consistency, and the schema contracts that define valid input data
• Take prototypes through to MVP, and stay with them through that transition
• Contribute to deliverables and publications in our EU-funded research projects, working alongside energy experts, researchers and ML engineers

• Fluent English with strong communication skills — you can explain a method, and honestly its limitations, to ML colleagues, to power systems engineers, and to an operator deciding whether to rely on it
• Deep, hands-on machine learning and reinforcement learning expertise with strong PyTorch skills, including a clear sense of when these methods are not the right choice
• Strong Python and sound software engineering practice — research code that only its author can run is not a sufficient outcome here
• You can read a paper and reproduce it, including resolving what the authors leave unstated
• Rigour in experimental design: meaningful baselines, controlled comparisons, and appropriate scepticism toward your own preliminary results
• Self-directed, you don't need a detailed roadmap to make progress
• Low-ego and collaborative, and genuinely interested in learning at the intersection of machine learning and power systems
• Willing to own the full development cycle, from literature review through to a 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

It would be great if you

None of this is required, and nobody has all of it — any one is a useful signal.

• Have power systems knowledge: power flow, state estimation, dynamic operating envelopes, or the operational context of distribution system operators
• Have used grid simulation frameworks such as pandapower, PyPSA, OpenDSS or Grid2Op
• Have worked in an adjacent method area — optimization (AC optimal power flow, convex relaxations), time series forecasting, or graph neural networks
• Have MLOps or data engineering experience: experiment tracking, model deployment, streaming data (Kafka, MQTT), or time series at scale
• Have work in public — publications at venues such as NeurIPS, ICLR, PSCC or IEEE PES, or contributions to a large open-source codebase
• Speak German, which helps when working directly with Austrian and German network operators

• Research problems that are genuinely open, on infrastructure that matters
• Both a research and a product dimension: our EU Horizon projects provide structure, visibility and the opportunity to publish, and behind them stands a longer-term objective of establishing these capabilities as products
• Publicly visible open-source work through Maze
• A team spanning reinforcement learning, optimization, data and platform engineering, and power systems — with the domain expertise to sanity-check a result and the platform expertise to run it
• Our own compute cluster, on our own hardware in a Vienna datacenter: dedicated GPU capacity for experiments rather than a shared queue or a cloud budget to argue for
• Hybrid working: 2–3 days per week at our office in the herat of Vienna's 1st district, with minimal core hours
• Dedicated time and budget for R&D, conferences and professional development
• Choose your own hardware and equipment setup
• Fully paid Klimaticket, giving you unlimited access to public transportation and trains across Austria

We are looking for a Platform & Systems Engineer to own how our systems are designed, deployed and run, and to take our technology from research-grade to production-grade.

Our research output has outgrown its engineering foundation. The methods work; the path from a working method to a reliably deployed service is slow and depends on too few people. Closing that gap is the job, and there is no inherited playbook for it — you would be defining what good looks like here rather than maintaining someone else's definition.

Three things sit at the centre of the role. Architecture comes first: our larger repositories need deliberate structure — module boundaries, interfaces, a testing strategy — so that more people can work in parallel without colliding. This is the highest-value part of the job, and the part most easily deprioritised, because it never arrives with a deadline attached. Deployment is second, including a repeatable path for standing up demos and proofs of concept, which is how our work reaches customers and project reviewers. Infrastructure is third: we run our own hardware in a Vienna datacenter, 11 nodes under a single Kubernetes cluster, and everything a managed platform would abstract away is ours — etcd, storage, the network, node lifecycle, GPU enablement.

You would join a team that combines reinforcement learning and optimization, data and platform engineering, and power systems expertise. You do not need to know how power grids work, that knowledge sits with colleagues.

• Shape the architecture of our larger repositories: module boundaries, interfaces, testing strategy, and the decisions that keep a growing codebase maintainable as more people work in it
• Build out and harden our deployment stack — containerised services on Kubernetes, packaged with Helm, with Redis and related components behind them
• Own the demo and proof-of-concept path, so that a presentable, isolated instance with seeded data can be stood up in hours rather than days
• Operate our Kubernetes cluster on our own hardware: node lifecycle, GPU enablement, storage, networking, capacity planning and upgrades
• Build the infrastructure beneath our data pipelines — MQTT and Kafka brokers, topic and retention design, orchestration, storage, and the backend services and APIs through which results reach applications and domain experts
• Build and harden CI/CD, developer tooling, and the internal platform our ML engineers and researchers work on daily
• Work alongside ML engineers, researchers and power systems experts on industrial and EU-funded projects

• Fluent English with strong communication skills — you can make an architectural case to people whose priority is research output, and come away with agreement rather than compliance
• Strong software engineering fundamentals: you think in systems, interfaces and failure modes, and can look at a growing codebase and say what will hurt in a year and why
• Solid Python, with experience in a substantial codebase that several people work on at once
• Kubernetes at the operator level — you have upgraded a cluster, replaced a node and debugged a networking or storage problem, not only deployed workloads into a platform someone else runs. Helm charts authored, not just installed
• Self-directed, you don't need a detailed roadmap to make progress
• Low-ego and collaborative — much of this role means restructuring code that other people wrote and care about
• A degree in computer science or a related field, or equivalent practical experience
• Valid work permit for Austria

It would be great if you

None of this is required, and nobody has all of it — any one is a useful signal.

• Have run stateful infrastructure in production — Kafka and MQTT brokers especially, but also Redis or databases — with a real feel for persistence, delivery semantics and failure behaviour
• Have built out infrastructure-as-code and observability rather than inheriting someone else's
• Have worked with pipeline orchestration, time series at scale, GPU scheduling on Kubernetes, or Ray
• Have built lightweight PoC frontends, or know your way around power grids
• Speak German, which helps when working directly with Austrian and German network operators

• Real ownership: this role exists to change how we build and ship, with a mandate to make architectural decisions rather than implement someone else's
• Infrastructure you actually own — our own Kubernetes cluster on our own hardware, rather than a managed platform with the interesting parts abstracted away
• A team spanning reinforcement learning, optimization, data and platform engineering, and power systems, with the domain expertise to tell you why a constraint exists
• Work at the interface of academia and industry: our EU Horizon projects mean the systems you build are used by research consortia as well as by customers
• Work on critical infrastructure that matters: the energy transition is, at heart, a systems problem
• Hybrid working: 2–3 days per week at our office in Vienna's 1st district, with minimal core hours
• Dedicated time and budget for R&D, conferences and professional development