At Aleph Alpha, we foster a culture built on ownership, autonomy, and empowerment. Teams and individual contributors are trusted to take responsibility for their work and drive meaningful impact. We maintain a flat organizational structure with efficient, supportive management that enables quick decision‑making, open communication, and a strong sense of shared purpose. ## Your Responsibilities
Co-Own data pipelines end-to-end: Design, build, and maintain the infrastructure that sources, processes, deduplicates, filters, and prepares pre-training corpora at scale. Own the conversion from curated corpora to training-ready streaming formats. - Curate and compose data mixtures: Define and iterate on the data blends used for pre-training - balancing domains, languages, quality tiers, and licensing requirements to maximise model capability. - Build data quality tooling: Develop classifiers, heuristics, and analysis frameworks that measure and enforce data quality across terabyte-scale corpora. Monitor pipeline health and data quality metrics at scale. - Close data gaps: Work with evaluation and post-training teams to identify where model weaknesses trace back to data coverage, then source or generate the data needed to address them. - Collaborate with post-training: Partner closely with the post-training team to ensure pre-training data decisions support downstream fine-tuning, alignment, and deployment goals - data choices upstream shape what's possible downstream. - Ensure deep, high-quality coverage of German-language corpora - this is core to our value proposition, not an afterthought. - Design and run ablation studies to validate data choices - measuring how changes in composition, filtering, or sourcing affect pre-training evaluation metrics and downstream capabilities. - : Maintain data lineage and provenance so the team knows exactly what went into each training run. ##
Co-Own German-language data:
Establish data-to-performance signal:
Take data transparency seriously
Your Profile
Basic Qualifications
Track record of shipping impactful technical work - whether that's research, infrastructure, or both. - Strong Python skills and comfort with data engineering and ML infrastructure, including experience with deep learning frameworks, workflow orchestration, object storage, columnar data formats, and distributed processing. - Ability to reason about what a dataset contributes to model training and whether it matters - not just process data, but understand it. - Ownership mentality: you see problems through from diagnosis to solution to deployment. - Willingness to relocate to Heidelberg or travel at least fortnightly. ## Preferred Qualifications
Experience with large-scale data processing for ML, including corpus sourcing, curation, cleaning, deduplication, and filtering. - Familiarity with data quality methods: classifier-based filtering, heuristic scoring, perplexity-based selection, and decontamination. - Understanding of foundation model training - how data composition, scale, and mixing ratios affect capabilities. - Experience with web-scale data sourcing and crawl processing (e.g., Common Crawl, WARC pipelines). - Rust proficiency (parts of our data pipeline are performance-critical). - Infrastructure knowledge - experience with Kubernetes, container orchestration, or cloud-native ML infrastructure. - PhD in machine learning, NLP, data engineering, or a related field (valued but not required - we care about what you can do). - Bonus, but not required: German language proficiency can be helpful for curating and assessing German-language data. ## Compensation and Benefits
Become part of an AI revolution! - 30 days of paid vacation
Access to a variety of fitness & wellness offerings via Wellhub
Mental health support through nilo.health
Substantially subsidized company pension plan for your future security
Subsidized Germany-wide transportation ticket
Budget for additional technical equipment
Flexible working hours for better work-life balance and hybrid working model
