Machine Learning Infrastructure Engineer (MLOps)

The power grid is changing faster than the software running it. Renewables are flooding supply, prices swing wildly, and millions of EV batteries sit idle on driveways every night, completely disconnected from the markets that need them most. Enkel is the platform that closes that gap. We integrate directly with energy markets and connect distributed EV assets to the grid in real time - turning idle batteries into a network. For consumers, that means lower bills and earnings from their car. For grid operators, it means distributed flexibility at scale, without building a single new power plant. We are a team with deep roots in energy markets, software, engineering and mathematics. Based in Copenhagen, we are building the connection between consumers and the grid: a layer that every stakeholder in the energy transition needs. This is infrastructure with physical consequences. Our models decide when real cars charge and what real customers pay, and the fleet they control is qualified to deliver balancing services to the grid. We are looking for an ML Infrastructure Engineer to work together with our highly experienced research team to ensure our models are trained, scaled, and reliably deployed. You will build the scaffolding needed to track results for rapid, reproducible iteration, and approach both data management and production inference with care - ensuring our latest validated models are deployed safely and reliably. You care about the details of reliability and you are comfortable taking responsibility for the pipeline from raw data to production inference. What you will do: Set up production inference: Deploy trained models into our cloud environment, managing database access and ensuring model decisions are cleanly and reliably written to production tables.Build reproducible environments: Set up experiment tracking and database storage for training results to ensure every model iteration is fully reproducible.Manage our data: Prepare and manage the data pipelines required for training, handling ingestion, versioning, and processing.Scale training infrastructure: build parallel workloads across own hardware and cloud environments.Bridge research and engineering: Work alongside our research team, providing the infrastructure they need to iterate effectively while maintaining system stability. What we offer Meaningful work in the energy transition, with a company that has proven traction.A high degree of autonomy within a capable technical team that values clear thinking and pragmatic execution.A flat structure with straightforward decision-making, while maintaining careful engineering standards.Competitive salary, bonus and equity in the company.Relocation support if required. We know great minds come from many different paths and encourage applications from all backgrounds.