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About the Role
Develop agents that access information across large document collections, investigate how to assess completeness, contradictions, and source quality, and ensure that answers remain traceable and uncertainties are visible. Alternatively, develop forecasting models for advertising block performance, assess uncertainties, measurement errors, and the significance of your models, and process large amounts of data with Spark on our cluster, investigating where data quality or processing affects results. In both areas, you will turn a good question into something that works: experiment and evaluate, take responsibility, work with engineering, and support other teams.
Responsibilities
- Develop agents that access information across large document collections.
- Investigate how to assess completeness, contradictions, and source quality.
- Ensure that answers remain traceable and uncertainties are visible.
- Develop forecasting models for advertising block performance.
- Assess uncertainties, measurement errors, and the significance of your models.
- Process large amounts of data with Spark on our cluster, investigating where data quality or processing affects results.
- Formulate hypotheses, design meaningful tests, and learn from results, even if they do not meet your expectations.
- Independently develop your approach to a robust prototype.
- Get feedback and work with your team on robust solutions.
- Accompany the transfer of your prototype into a product and contribute your knowledge of the problem and solution.
- Assess data questions, demonstrate meaningful use cases for AI, and provide a well-founded recommendation or prototype as needed.
Requirements
- Solid technical foundations: Bring knowledge in computer science, software development, or data science – acquired through study, doctorate, or practical experience.
- You can justify your choice of methods and critically evaluate results.
- Understanding of AI: You don't have to train models in PyTorch. However, explain, for example, the difference between Prefill and Decode and its impact on latency and costs.
- Independent AI-native Development: Use coding agents to develop faster.
- Validate the generated code, test the results, and understand how your application and its components function.
- Structured Experimentation: Formulate clear specs, define a definition of done, and use appropriate tests and evaluations to check the quality of your solutions.
- Open Collaboration: Share knowledge, discuss technical decisions, and provide respectful, direct feedback.
- A portfolio, a side project, or a demo repo – something that shows how you approach technical problems and implement solutions.
Skills
- Informatik
- Softwareentwicklung
- Data Science
- AI
- Coding-Agenten
- NLP/Computerlinguistik
- Agentic Retrieval
- Entwicklung von LLM-Agenten
- Python
- Pydantic AI
- TypeScript
- vLLM/SGLang
- Spark
- Trino
- StarRocks
- Postgres
- Prefect
- OpenTelemetry
- Docker/Kubernetes
- Ray
- LangChain
- Airflow
Location
- Berlin
- Remote
Work Type
- Remote Work
- Hybrid
Experience Level
- Practical experience
Education Level
- Study
- Doctorate
Benefits
- 30 vacation days plus time off on December 24th and 31st
- Smart Fridays with the option of a four-day week
- Deutschlandticket
- JobRad
- Sports and health offers
- Support for mental health
- Corporate Benefits
- Access to RTL+
About the Company
- AI makes us faster. It doesn't do our thinking for us.
- What AI produces, we check. We are responsible for everything that goes out.
- We are fast at trying things out, thorough at delivering.
Equal Opportunity
- smartclip is committed to a diverse and inclusive work environment. All qualified applicants will be considered for employment regardless of race, color, nationality, age, gender, gender identity, religion, sexual orientation, disability, or other personal characteristics.