Senior Machine Learning

EnopAI GmbH & Co. KG Ludwigsvorstadt

Stellenbeschreibung:

Your mission

  • Deliver forecasting and optimization that reduce energy costs and emissions for real systems.
  • Own problems end-to-end: from hypothesis to model to production and measurable impact.
  • Improve our time-series forecasting models. Design and run experiments to improve predictions (demand, PV, prices, weather-derived features). Use proper backtests, leakage controls, and business KPIs.
  • Optimization modeling. Extend our MILP models to new features like EV charging, market trading (day-ahead / intraday), tariffs, and network limits. Encode constraints and objectives that reflect physics and economics.
  • Simulation & evaluation. Build scenario simulations to compare strategies and assess reliability.
  • Productization. Ship models behind robust APIs and scheduled jobs with tests, CI, and observability.
  • Continuous growth. Expand your expertise in data science, optimization, and software engineering through mentorship, hands-on collaboration, and exposure to challenging, real-world problems in energy and technology.

Your profile

  • 5+ years total experience in applied Machine Learning, Data Science, Mathematical Optimization and/or Operations Research.
  • Experience with exploratory data analytics and a strong intuition for designing experiments and deriving insights from data – especially working with time series datasets.
  • Skilled at identifying issues and iteratively enhancing the performance of ML and optimization models in real world.
  • Machine Learning skills (e.g. PyTorch/Sklearn/Tensorflow)
  • Optimization experience (e.g. Pyomo, MILP solvers like Gurobi/HiGHS)
  • Production-grade Python (clean, testable, reviewable code) and software engineering tools (Github, Pull Requests). MLOps experience is a plus.
  • Bonus: Energy domain knowledge like BESS, PV, EV charging, demand charges, market participation (EPEX/ENTSO-E context), network/transformer limits.
  • Great at communicating in a concise and intuitive way to the rest of the team.
  • Thrives in a fast-moving environment and comfortable switching between modeling, simulation, and engineering as priorities shift. Pragmatic, driven and impact-oriented.

Why us?

  • The excitement of working in a driven start-up team, where your contribution truly matters.
  • A secure position in an innovative company with long-term prospects
  • Competitive salary with additional benefits such as EGYM Wellpass
  • Great team culture & events: Summer parties, Christmas celebrations, and regular team gatherings
  • Prime location: Office right next to Theresienwiese in Munich (home office days possible)
  • Creative freedom: The opportunity to contribute your ideas and actively shape the future of the company and the field of energy optimization
  • Keep growing by solving hard, real-world energy problems alongside senior teammates, through hands-on experimentation and production-scale systems.

About us


EnopAI is an early-stage software startup, originating from Rudolf Hörmann GmbH & Co. KG. We empower leading EMS integrators and hardware vendors with cloud-to-cloud "optimization-as-a-service," seamlessly linking on-site control systems to our AI engine.

Our real-time platform ingests live data streams—solar PV, battery storage, EV chargers, CHP units—and instantly tunes every parameter to drive down costs and carbon emissions. Proven at the Hörmann production site and a growing roster of industrial partners, EnopAI delivers tangible stability, savings and a smoother path through Europe's energy transition.

We're inviting passionate individuals to join us at the forefront of energy innovation—where every line of code you write drives cleaner, more resilient grids across Europe.

NOTE / HINWEIS:
EnglishEN: Please refer to Fuchsjobs for the source of your application
DeutschDE: Bitte erwähne Fuchsjobs, als Quelle Deiner Bewerbung

Stelleninformationen

  • Veröffentlichungsdatum:

    29 Nov 2025
  • Standort:

    Ludwigsvorstadt
  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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