Job
- Level
- Junior
- Location
- Munich
- Working Model
- Onsite
- Job Field
- Data
- Employment Type
- Full Time
- Contract Type
- Permanent employment
Job Summary
In this role, you develop scalable AI solutions for search and ranking, combining classical information retrieval methods with deep learning, and support projects from prototyping to production deployment.
Job Technologies
Your role in the team
- Our Search Team within CHECK24 AI Forge develops scalable AI solutions for Search and Ranking across various CHECK24 products.
- Search is a central entry point and navigation hub for our clients, covering complex domains - from e-commerce product search and web-like navigation to travel search for hotels, destinations, and other products.
- Search and ranking are among the most challenging disciplines in applied machine learning.
- A good system must understand user intent, retrieve relevant candidates, rank them effectively, and meet high standards for latency, scalability, and measurable customer value.
- Many of the questions we work on are technically complex and cannot be answered with standard solutions — an analytical, hypothesis-driven approach as well as solid engineering are required.
- You work on production systems that combine traditional information retrieval methods, deep learning, embeddings, and large language models, and you oversee the entire lifecycle - from initial idea through evaluation to deployment, monitoring, and continuous improvement.
- A particular focus is on agentic workflows: the design, evaluation, and productive deployment of systems that independently utilize tools and solve multi-stage tasks.
- You create robust evaluation datasets and metrics and validate improvements through offline evaluations, user signals, and A/B tests.
- You research promising approaches in Information Retrieval, Recommender Systems, and Large Language Models and translate them into measurable customer benefits.
- In model development, you focus on training, fine-tuning, and systematic evaluation—with an emphasis on Semantic Search, Relevance Prediction, and Learning-to-Rank.
- You develop and improve ML services for retrieval, ranking, query understanding, and classification.
- In hybrid search solutions, you combine lexical retrieval, embeddings, neural models, and LLM-based components.
- Together with the team, you will transition prototypes into production-ready services using technologies such as Python, FastAPI, Docker, PyTorch, and vector databases.
- For efficient prototyping, testing, and continuous improvements, you utilize modern AI-powered and agentic engineering workflows.
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Our expectations of you
Education
- You have successfully completed a university degree (usually a Master's) in Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, or a comparable quantitative field, with strong grades in quantitative subjects.
Qualifications
- A solid understanding of ML fundamentals and systematic model evaluation enables you to assess model performance thoroughly, test hypotheses, and justify improvements transparently.
- Solid classical ML skills such as Gradient Boosting, Regression, and Feature Engineering are evident in your CV — beyond LLM or RAG applications.
- Knowledge of Information Retrieval, Semantic Search, Recommender Systems, or Learning-to-Rank is advantageous.
- Good English skills (at least C1) complete your profile; German skills are a plus but not a requirement.
Experience
- You have at least one year of relevant practical experience in Machine Learning or Data Science, including work student activities, internships, or personal projects.
- Strong Python skills and practical experience with ML or Deep Learning frameworks like PyTorch are part of your technical toolkit.
- You have practical experience with LLM-based applications, LLM evaluation, tool use, or agent workflows.
- Practical experience with APIs (e.g., FastAPI), containers, and Git - ideally including a self-deployed service - complements your interest in building complete, production-ready systems.
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What we offer
- Your commitment pays off - we nurture your talent and offer opportunities to accelerate your career.
- Take ownership of your topics, contribute your own ideas, try new approaches, and grow step by step in responsibility for productive search and ranking systems.
- Benefit from internal training sessions with specialized coaches as well as an annual budget for external professional development.
- Experience team spirit in everyday work life and beyond – during shared lunches, a game of table football, or after-work activities, and celebrate successes at regular company and team events.
- Work in an inspiring environment with a modern office and high-quality equipment such as MacBooks and height-adjustable desks.
- On Fridays, working from home is optional - those who spend the day in the office accrue additional vacation days beyond the 28 days.
- Travel comfortably to the office with your free Germany ticket or a subsidized company bike.
- Enjoy a fresh, free lunch twice a week and take the opportunity to network with colleagues.
- Stay active with our internal sports courses or take advantage of our subsidized external sports program, including EGYM Wellpass.
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Benefits
Work-Life-Integration
Health, Fitness & Fun
Food & Drink
Higher Take-Home Pay
Topics You Will Work On
Job Locations
About Your Employer
CHECK24 Gruppe
CHECK24 is one of Germany's leading online companies and the country's largest comparison portal. And we're growing - with you. If you want to move something, you've come to the right place. Experience the dynamics of e-commerce, agile processes, flat hierarchies and steep development curves.
Description
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Banking, Finance, Insurance, Internet, IT, Telecommunication
Employer reviews
by devworkplaces.com
Total
(3 Reviews)4.1
Engineering
3.8Workingconditions
4.2Career Growth
4.3Culture
4.2
Junior Data Scientist Search / AI Forge
- Location
- Munich
- Working Model
- Onsite
- Diversity
- Open for all genders
