(Junior) Data Scientist Search / AI Forge

Role overview

Benefits

• check_circle Your commitment pays off – we support your talent and provide opportunities to grow your career quickly
• check_circle Take ownership of your topics, contribute your own ideas, try new approaches and gradually assume responsibility for production Search and Ranking systems
• check_circle Benefit from internal training with specialized coaches as well as an annual budget for external professional development
• check_circle Experience team spirit in your daily work and beyond, whether over lunch, a game of table football or after-work activities, and celebrate our successes at regular company and team events
• check_circle Work in an inspiring environment with a modern office and high-quality equipment such as MacBooks and height-adjustable desks
• check_circle Fridays are home-office optional – and spending them in the office can earn you additional vacation days on top of your 28 days
• check_circle Get to the office conveniently with your free Deutschlandticket or a subsidized company bicycle
• check_circle Enjoy a free fresh lunch twice a week and use the opportunity to connect with your colleagues
• check_circle Stay active with our internal sports classes or take advantage of our subsidized external sports offering, including EGYM Wellpass

About the company

• check_circle You have successfully completed a degree (typically a Master's) in computer science, mathematics, statistics, physics, electrical engineering or a comparable quantitative field, with strong grades in quantitative subjects
• check_circle At least one year of relevant hands-on experience in machine learning or data science is part of your background, including relevant experience gained through working-student positions, internships or your own projects
• check_circle Strong Python skills and practical experience with machine-learning or deep-learning frameworks such as PyTorch are part of your technical toolkit
• check_circle Solid classical machine-learning skills like gradient boosting, regression and feature engineering are evident in your CV, beyond LLM or RAG applications
• check_circle You have practical experience with applications based on Large Language Models, LLM evaluation, tool use or agentic workflows
• check_circle A solid understanding of machine-learning fundamentals and systematic model evaluation enables you to assess and improve model performance
• check_circle Practical experience with APIs (e.g. FastAPI), containers and Git, ideally a deployed service, complements your interest in building complete production services
• check_circle Knowledge of information retrieval, semantic search, recommender systems or learning-to-rank approaches is a plus
• check_circle You have English proficiency at C1 level or higher; German is a plus but not required
• check_circle You develop and improve machine-learning services for retrieval, ranking, query understanding and classification
• check_circle Hybrid search solutions combine lexical retrieval, embeddings, neural models and components based on Large Language Models
• check_circle Model development covers training, fine-tuning and systematic evaluation for semantic search, relevance prediction and learning-to-rank use cases
• check_circle You create reliable evaluation datasets and metrics and validate improvements through offline evaluations, user signals and A/B tests
• check_circle Production-ready services are built from prototypes using technologies such as Python, FastAPI, Docker, PyTorch and vector databases
• check_circle Modern AI-assisted and agentic engineering workflows enable efficient prototyping, testing and continuous improvement
• check_circle You research promising approaches in information retrieval, recommender systems and Large Language Models and translate them into measurable customer impact