Backing and Momentum:
With €9M in pre-seed funding from top-tier investors including Balderton Capital, XTX Ventures, and Hector Foundation—and support from leaders at Hugging Face, DeepMind, and Silo AI—we’re moving rapidly toward commercialization.

About the Role

Location:
Berlin, Freiburg

Employment Type:
Full-time

Department:
All

Overview:
You'll play a critical role in ensuring the reliability, efficiency, and performance of our models at scale. You'll work on designing robust evaluation systems, optimizing model performance, and ensuring our infrastructure can support cutting-edge AI development. You'll also lead initiatives around data collection, benchmarking, and systematic evaluation to drive continuous model improvement and support our open-source community.

Key Responsibilities:

• Systems Engineering & Performance Optimization:
  Design, implement, and maintain scalable infrastructure to support large-scale model training and evaluation. Identify and solve complex bottlenecks in our training and inference pipelines for efficiency and speed.

• Model Evaluation & Benchmarking:
  Develop comprehensive evaluation frameworks to assess model performance, robustness, and reliability. Design and maintain benchmarking protocols to compare our models against industry standards and track progress over time.

• Data Collection & Curation:
  Collaborate on efforts to collect, curate, and manage the high-quality datasets that are essential for training and evaluating our models.

• Open Source & Developer Experience:
  Act as a key steward of our core open-source packages (tabpfn, tabpfn-extensions). Improve the developer experience through excellent documentation, clean APIs, and a seamless contribution process for our community.

• Collaborate on Hard Problems:
  Work closely with our ML researchers to translate deep technical challenges into well-designed, scalable software systems.

Qualifications:

• Exceptional software engineering fundamentals and expert-level proficiency in Python, demonstrated through 5+ years of experience or an advanced degree (MS/PhD) in Computer Science or a related field.
• A proven track record of architecting and building complex, scalable software, preferably for data processing, automated testing, or distributed systems.
• Deep, practical knowledge of the modern ML ecosystem (PyTorch, scikit-learn, etc.) and a genuine interest in applying systems thinking to solve hard problems in AI.
• Strong understanding of the entire machine learning lifecycle (MLLC) from data ingestion and preparation to model deployment, monitoring, and retraining. Familiarity with MLOps principles and best practices (e.g., reproducibility, versioning, automation, continuous integration/delivery for ML).

Benefits:

• Competitive compensation package with meaningful equity
• 30 days of paid vacation + public holidays
• Comprehensive benefits including healthcare, transportation, and fitness
• Work with state-of-the-art ML architecture, substantial compute resources, and a world-class team