{"id":1103579,"url":"https://alion.io/job/enlite-ml-research-engineer-power-systems","title":"ML Research Engineer - Power Systems","company":{"id":26882,"name":"Enlite","domain":"enlite.ai","url":"https://alion.io/company/enlite","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Join","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Vienna, Austria"],"countries":["AT"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":51000,"max_usd":119000,"period":"year","method":"role_country_seniority_unknown","sample_n":32},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"Machine Learning","optional":false},{"name":"MQTT","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-09-21T13:53:53Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-10-30T01:27:00Z","board_verified":true,"closed_at":null,"days_open":38,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":38},"description":"About enliteAI\nenliteAI builds AI and optimization systems for critical infrastructure, with a focus on electricity networks. We work with network operators and industrial partners on problems where the decisions carry real consequence: how a distribution grid is operated, how much capacity it can safely carry, and how flexible resources at the grid edge are coordinated. Our work runs from applied research through to systems that operate on real measurement data.\nWe are part of several EU Horizon research programmes - AI4REALNET, AI-EFFECT and INSIEME - and we maintain Maze, an open-source framework for simulation based reinforcement learning. Our team combines reinforcement learning, optimization, data engineering and power systems expertise, and sits deliberately between academic research and industrial deployment. We are based in Vienna.\nWe are a small team and work like one: cross-disciplinary by default, low-ego, and more interested in whether something holds up than in who proposed it.\nThe Role\nWe 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.\nDistribution 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.\nWhat 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.\nWe 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.\nAlongside this you will contribute to Maze, extending it toward energy and infrastructure applications. That work is publicly visible and used beyond enliteAI.\nYou 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.\nThe 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.\nTasks\nDevelop 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\nReview and assess the state of the art, judging which published approaches are implementable at industrial scale and which remain limited to small test cases\nDesign and run experiments with meaningful baselines, ablations and realistic data regimes, validated against established simulation environments (pandapower, PyPSA, OpenDSS, Grid2Op)\nContribute to core components of Maze and extend it toward energy and infrastructure applications\nOwn 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\nTake prototypes through to MVP, and stay with them through that transition\nContribute to deliverables and publications in our EU-funded research projects, working alongside energy experts, researchers and ML engineers\nRequirements\nFluent 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\nDeep, 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\nStrong Python and sound software engineering practice - research code that only its author can run is not a sufficient outcome here\nYou can read a paper and reproduce it, including resolving what the authors leave unstated\nRigour in experimental design: meaningful baselines, controlled comparisons, and appropriate scepticism toward your own preliminary results\nSelf-directed, you don't need a detailed roadmap to make progress\nLow-ego and collaborative, and genuinely interested in learning at the intersection of machine learning and power systems\nWilling to own the full development cycle, from literature review through to a deployed MVP\nA degree in computer science, energy informatics, electrical engineering, applied mathematics or a related field, or equivalent practical experience\nValid work permit for Austria\nIt would be great if you\nNone of this is required, and nobody has all of it - any one is a useful signal.\nHave power systems knowledge: power flow, state estimation, dynamic operating envelopes, or the operational context of distribution system operators\nHave used grid simulation frameworks such as pandapower, PyPSA, OpenDSS or Grid2Op\nHave worked in an adjacent method area - optimization (AC optimal power flow, convex relaxations), time series forecasting, or graph neural networks\nHave MLOps or data engineering experience: experiment tracking, model deployment, streaming data (Kafka, MQTT), or time series at scale\nHave work in public - publications at venues such as NeurIPS, ICLR, PSCC or IEEE PES, or contributions to a large open-source codebase\nSpeak German, which helps when working directly with Austrian and German network operators\nBenefits\nResearch problems that are genuinely open, on infrastructure that matters\nBoth 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\nPublicly visible open-source work through Maze\nA 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\nOur 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\nHybrid working: 2-3 days per week at our office in the herat of Vienna's 1st district, with minimal core hours \nDedicated time and budget for R&D, conferences and professional development\nChoose your own hardware and equipment setup\nFully paid Klimaticket, giving you unlimited access to public transportation and trains across Austria\nJob Types: Full-time or Part-time (min. 30h)\nSalary Range: > €65,000 annually (based on full-time), depending on experience and qualifications.\nTags: Machine Learning, Research Engineer, Reinforcement Learning, Maze, PyTorch, Python, State Estimation, Dynamic Operating Envelopes, Power Systems, Smart Grids, Energy Optimization","description_format":"text","description_chars":8021,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[{"language":"German","level":"All levels","optional":false},{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":["Hybrid work","Professional development"],"hiring_locations":[{"name":"Austria","iso":"AT","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Electrical & Electronic Engineering","Reinforcement Learning","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-09-22T03:14:38Z"}],"visa":[],"liveness":{"score":37,"band":"fade","label":"Fading","p_open":1,"p_active":0.492,"p_room":0.75,"age_days":37,"expected_fill_days":40,"reasons":["conf:5","win:late"],"computed_at":"2026-10-29T05:49:05Z"},"pay":null,"html_url":"https://alion.io/job/enlite-ml-research-engineer-power-systems","json_url":"https://alion.io/job/enlite-ml-research-engineer-power-systems.json","meta":{"generated_at":"2026-10-30T05:37:28Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":7824,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-31T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":26882},"rest":"https://alion.io/mcp/rest/get_company?id=26882"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fenlite-ml-research-engineer-power-systems"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fenlite-ml-research-engineer-power-systems"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fenlite-ml-research-engineer-power-systems"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/enlite-ml-research-engineer-power-systems\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fenlite-ml-research-engineer-power-systems"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":1103579}},{"tool":"search_jobs","arguments":{"company":"enlite.ai","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":1103579},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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