# Senior ML Engineer, Energy Forecasting at Talcom

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> Responsibilities: - Design and implement production-grade fo

- **Company:** Talcom (https://jobs.trythestack.co/companies/talcom)
- **Location:** Netherlands
- **Work arrangement:** On-site
- **Employment type:** Full-time
- **Experience level:** 5–10 years
- **Visa sponsorship:** Offered, according to the listing
- **Key skills:** Python, Time Series Forecasting, Machine Learning, PyTorch, TensorFlow, Docker, Kubernetes, Data Pipelines, Predictive Modeling, MLOps, Distributed Processing, Spatio Temporal Modeling, Probabilistic Forecasting, Graph Neural Networks, Transformers, NumPy
- **Posted by employer:** 2026-06-08
- **Added to The Stack:** 2026-06-30
- **Status:** May no longer be active (posted more than 60 days ago)
- **Apply:** https://talcom.zohorecruit.eu/jobs/Careers/86328000000987005/Senior-ML-Engineer-Energy-Forecasting (the employer's own application page)
- **Human page:** https://jobs.trythestack.co/jobs/senior-ml-engineer-energy-forecasting-talcom

## About the role

Responsibilities:
- Design and implement production-grade forecasting models for renewable generation, consumption, and market signals.
- Build and maintain end-to-end ML pipelines: data ingestion (weather, market, telemetry), feature engineering, training, validation, and deployment.
- Deploy and operate models at scale using containerization and orchestration tools (Docker, Kubernetes) and cloud-native patterns.
- Validate data quality and model assumptions, run backtests and probabilistic evaluations, and quantify business impact of model improvements.
- Collaborate with trading, engineering, and ops teams to integrate forecasts into automated trading and optimization systems.
- Instrument monitoring, alerting, and retraining workflows to ensure reliability and continuous improvement.

Requirements:
- 5+ years in ML or data science with proven experience in time series and probabilistic forecasting.
- Strong Python skills and familiarity with ML frameworks (PyTorch, TensorFlow) and numerical libraries (NumPy).
- Hands-on experience deploying ML in production, including Docker, Kubernetes, and cloud-native data stores (Postgres, ClickHouse, Redis).
- Knowledge of spatio-temporal modeling, graph neural networks or transformers for time series is a plus.
- Experience with distributed processing, streaming systems, and MLOps best practices.
- Strong engineering mindset, ability to validate assumptions end-to-end, and clear communication with cross-functional teams.

## Responsibilities

Design and deploy production-grade forecasting models for renewable energy production, consumption, and weather conditions. Build end-to-end ML pipelines and collaborate with stakeholders to shape the platform's machine learning architecture.

## Requirements

Requires 5+ years of experience in Machine Learning or Data Science with deep expertise in time series forecasting and Python. Must have experience building production ML systems and data pipelines using Docker and Kubernetes.

## FAQ

- **Is this Senior ML Engineer, Energy Forecasting role still open?** It was posted on 2026-06-08. Listings more than 30 days old may no longer be active, so check Talcom's site before applying.
- **Does Talcom offer visa sponsorship?** This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Talcom's site.
- **Where is this role located?** The listing gives the location as Netherlands. It is marked on-site.
- **What skills does this role ask for?** The listing names: Python, Time Series Forecasting, Machine Learning, PyTorch, TensorFlow, Docker, Kubernetes, Data Pipelines, Predictive Modeling, MLOps, Distributed Processing, Spatio Temporal Modeling, Probabilistic Forecasting, Graph Neural Networks, Transformers, NumPy.

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## Notes for agents

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