Data Scientist ML/LLM (m/w/d)
Beschreibung
For our client's team, we are looking for a talented engineer with a passion for research and development of machine learning pipelines that innovatively store and utilize our domain-specific knowledge. If you have solid expertise in ML and engineering and enjoy conducting independent research, we look forward to receiving your application.
Responsibilities:
- Solve central challenges in ML and data engineering, including model deployment, backend/frontend engineering, model evaluation, and fine-tuning.
- Develop methods for the semantic interpretation and automated redundancy elimination of proprietary documents.
- Plan and conduct experiments to improve the knowledge base for LLM agents.
- Build pipelines for data collection, document preparation, and LLM evaluation.
- Analyze and optimize pipeline performance.
Requirements
- Master’s or PhD degree in Computer Science, Data Science, Statistics, Linguistics, or related fields.
- At least one year of professional experience in machine learning or related fields.
- Technical expertise in deploying and evaluating ML models in production environments or in ML/LLM research.
- Strong programming skills in Python and deep learning frameworks like PyTorch, TensorFlow, or JAX.
- Fluency in both German and English.
Benefits
- International environment with diverse perspectives and a global network.
- Exciting innovation projects paving the way for new technological developments.
- Hybrid model for a flexible working style between the office and home office.
Wir wertschätzen Vielfalt und begrüssen daher alle Bewerbungen - unabhängig von Geschlecht, sozialer Herkunft, Religion, Alter und Identität. Zur leichteren Lesbarkeit und besseren Verständlichkeit verwenden wir nur eine Gender-Form. Selbstverständlich sind im jeweiligen Kontext alle Genderformen gleichermassen gemeint.
Bewerben Als PDF herunterladen
Wir wertschätzen Vielfalt und begrüssen daher alle Bewerbungen - unabhängig von Geschlecht, sozialer Herkunft, Religion, Alter und Identität. Zur leichteren Lesbarkeit und besseren Verständlichkeit verwenden wir nur eine Gender-Form. Selbstverständlich sind im jeweiligen Kontext alle Genderformen gleichermassen gemeint.
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