Machine Learning Engineer (Forecasting)

TL;DR

Machine Learning Engineer (Forecasting): Developing and deploying high-accuracy wind and solar forecasting models with an accent on time-series analysis, spatiotemporal data, and production reliability. Focus on building end-to-end ML pipelines, diagnosing model behavior, and improving forecasting performance in a high-impact renewable energy environment.

Location: Hybrid role with offices in Tallinn and Rome; preference is given to candidates based in Tallinn or Rome.

Company

hirify.global builds high-accuracy wind and solar forecasting solutions using advanced machine learning and weather modeling to accelerate the transition to a net-zero future.

What you will do

• Support model deployments including scheduled inference, retraining workflows, and monitoring.
• Investigate and resolve data or model issues in production environments.
• Contribute to forecasting model development through experimentation, feature engineering, and evaluation.
• Clean, analyze, and transform complex meteorological and time-series datasets.
• Run experiments to steadily improve model performance and robustness.
• Ensure clarity and reproducibility through documentation and structured workflows.

Requirements

• 3-5 years of end-to-end ML experience (data to model to deployment).
• Solid Python skills and experience with PyTorch or comparable deep learning frameworks.
• Intuition for time-series modeling or spatiotemporal data.
• Experience deploying models (batch jobs, APIs, pipelines) and debugging production behaviors.
• Ability to work effectively in a dynamic environment with evolving tooling and datasets.
• Clear communication skills and enthusiasm for renewable-energy challenges.

Nice to have

• Exposure to weather/NWP or other scientific datasets.
• Experience with large-array formats like Zarr or Icechunk.
• Familiarity with model monitoring, retraining cadence, or drift detection.
• Interest in probabilistic or ensemble forecasting.

Culture & Benefits

• Work on challenging and meaningful forecasting problems with direct impact on clean energy.
• High autonomy and clear ownership within a transparent, fast-moving team.
• Collaborative environment with experienced data scientists and engineers.
• EU-friendly work environment with a strong emphasis on growth and learning.
• Visibility to leadership and high-ownership work culture.