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Enkel is building a platform that connects distributed EV assets and renewable energy to electricity markets in real time, enabling grid flexibility at scale without new infrastructure. The company integrates directly with energy markets and turns idle EV batteries into a distributed network resource.
As ML Infrastructure Engineer, you will own the infrastructure that takes research models from experimentation to reliable production deployment. Your core responsibilities include:
**Production Inference & Deployment**: Deploy trained models into cloud environments, manage database access, and ensure model decisions are cleanly written to production systems with high reliability.
**Experiment Tracking & Reproducibility**: Build systems for tracking training results, managing experiment metadata, and ensuring every model iteration is fully reproducible and auditable.
**Data Pipeline Management**: Design and maintain data pipelines for training, including ingestion, versioning, validation, and processing. You'll handle both raw data ingestion and feature engineering infrastructure.
**Training Infrastructure**: Scale model training across both on-premise hardware and cloud resources, optimizing for parallel workloads and resource efficiency.
**Research-Engineering Bridge**: Work closely with the research team to understand their needs, provide infrastructure that enables rapid iteration, and maintain system stability and engineering standards.
You'll care deeply about reliability, reproducibility, and the details of how data flows from raw sources through training to production inference. The role requires comfort taking end-to-end responsibility for the ML pipeline and the ability to balance research velocity with production safety. The company operates with a flat structure, high autonomy, and pragmatic engineering standards. Based in Copenhagen with relocation support available.
