AI Engineer
Clera
München, Germany
8 days ago
Role details
Contract type
Internship / Graduate position Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
IntermediateJob location
München, Germany
Tech stack
Artificial Intelligence
Azure
Information Engineering
Python
Large Language Models
Terraform
Data Pipelines
Docker
Job description
Join a seed-funded, Y Combinator-backed pharmatech startup building an AI-native platform for pharmaceutical pricing and market access workflows. As an AI Engineer, you'll own meaningful parts of the AI and data pipelines and grow into a core engineer who shapes scalable AI solutions in real-world pharma contexts. This is a high-ownership role in a fast-moving, startup-minded environment where reliability and real-world impact are central to the mission.
Requirements
Do you have experience in Terraform?, Do you have a Master's degree?, * At least 2 years of experience (internships included) in ML, data engineering, or a related role.
- Strong Python skills with a track record of solving non-trivial problems, especially in data pipelines and ML/LLMs.
- Experience deploying ML/data pipeline features to production and monitoring their performance.
- Proficiency with Docker, Azure cloud platform, and Terraform for infrastructure and deployment.
- Experience designing and implementing automated data quality validation and evaluation systems for data and LLM outputs.
- A genuine care for correctness, reliability, and real-world impact.
Nice to Have:
- Experience applying AI to pharma or healthcare workflows.
- Prior startup experience and comfort operating in high-ownership, fast-paced environments.
Benefits & conditions
- Own and evolve significant parts of the AI and data pipeline infrastructure.
- Solve complex problems across large-scale AI and data systems.
- Build end-to-end LLM-powered extraction and transformation pipelines.
- Implement and maintain automation workflows for crawling, ingestion, and modelling.
- Develop automated validation and evaluation systems for data quality and LLM outputs.
- Turn real pharma workflows into AI-native systems.
- Take full ownership of features from concept through to production.