---
title: Job Openings | GovHorizon
description: Join GovHorizon — engineering, machine learning, and applied AI roles for the team building anticipatory governance and regulatory intelligence.
---

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Open roles

# Current openings

We hire people who want to work at the intersection of AI, governance, and strategic intelligence. If one of the roles below fits, get in touch.

### Machine Learning / AI Engineer

Mid Level

Role

We are looking for a Machine Learning / AI Engineer to help build, improve, and operationalize AI capabilities and intelligent models within the GovHorizon platform — from data preparation through to production deployment.

Responsibilities

- Build, train, and optimize ML/AI models for strategic use cases (e.g. regulatory forecasting, text analysis, predictive intelligence).
- Prepare and clean data, run feature engineering, and validate input quality for models.
- Work with engineering teams to deploy models to production (APIs, MLOps pipelines).
- Monitor model performance and implement automated retraining, drift detection, and performance metrics.
- Document workflows, model architecture, and experiment results.
- Help operationalize MLOps practices and model lifecycle management tools.

Requirements

- 3–5 years of experience in Machine Learning, Data Science, or AI Engineering.
- Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, or equivalent).
- Knowledge of data pipelines, ETL/ELT, and orchestration frameworks (Airflow, Kubeflow, etc.).
- Experience with cloud ML services and deployment (Azure ML, Databricks, AWS SageMaker, GCP Vertex AI).
- Solid understanding of model performance metrics and result validation.

### AI Research Trainee

Entry Level

Role

We are looking for an AI Research Trainee to join GovHorizon's R&D team. You will support fine-tuning of language models for explainable scenario generation, working under the scientific coordination of our CTO and with access to European supercomputing infrastructure.

Responsibilities

- Support literature review on LLM fine-tuning and explainability methods, and help organize findings relevant to the project.
- Help configure and run experiments on supercomputing infrastructure.
- Contribute to testing source-traceability mechanisms in model outputs.
- Support evaluation and documentation of experiment results.
- Take part in internal technical reviews, following the progress of ongoing R&D work.

Requirements

- Currently completing, or recently completed, a degree in Machine Learning, Data Science, NLP or a related field.
- Basic proficiency in Python.
- Familiarity with machine learning fundamentals; prior exposure to LLMs is a plus, not a requirement.
- Curiosity about explainability and interest in applied research.

## Do you have what it takes?

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