Research Scientist - Pretraining-Daten für Foundation Models (all genders)
Tasks
- Build and integrate inference infrastructure for large models
- Design scalable data pipelines
- Develop synthetic data generation with large language models
- Evaluate AI research with application teams
- Extract filter deduplicate and evaluate data quality and toxicity
- Optimize inference frameworks for high performance computing
- Process web corpora for foundation model training data
- Publish research results in international conferences and journals
- Train foundation models on high performance computing
Perks/Benefits
- Access to high-performance infrastructure
- Collaborative team environment
- Deutschlandticket subsidy
- Diversity training
- Employer pension
- Professional development
Skills/Tech-stack
Apache Spark | Common Crawl | Dask | Data Deduplication | Data Generation | Data Quality | Data pipeline | Data quality assessment | Deep learning | Distributed Computing | Inference | Language Models | Language Processing | Large Language Models | Machine Learning | Model Serving | Natural Language | Natural Language Processing | PyTorch | Python | Quality Assessment | Ray | SGLang | Synthetic Data Generation | Synthetic data | Text Generation | Text generation inference | Toxicity Evaluation | VLLM | Web Scraping
Education
Roles
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