Internship - AI Metadata Augmentation
Roche is hiring an Internship - AI Metadata Augmentation. Level rates it ; you can apply on Roche.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions PyTorch, TensorFlow and Python among its tools and skills.
- Level classes it as an internship.
- Roche has 1,200+ other open roles on Level.
- Posted in the last few days.
AI in this role
Scale and optimize AI agents, prompt designs, and RAG pipelines for enterprise metadata management during a six-month internship.
At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.
The Position
Join our Enterprise Metadata Management team for a high-impact, six-month internship. Your core mission is to scale our AI metadata offering for multi-domain, enterprise-wide adoption. By adapting and fine-tuning AI agents, you will improve data discoverability, trust, and compliance in a highly regulated environment. You will not just be prototyping, you will take part of a project that moves directly into production, guided by experienced engineers.
The Opportunity
The Enterprise Metadata Management area is a cornerstone of our digital and agentic transformation, serving as the "brain" of our data ecosystem. In an era where data volume and complexity are accelerating, our mission is to transform raw information into a discoverable, high-trust asset. We focus on building and scaling AI-driven metadata solutions that move beyond manual tagging toward automated curation and intelligent discovery.
Our team sits at the intersection of cutting-edge engineering and strategic business value. We are responsible for developing technologically agnostic frameworks that allow for seamless integration across diverse platforms, ensuring that business lineage and data provenance are transparent and scalable at the enterprise level. By leveraging machine learning and automated curation, we enable the organization to understand not just what data we have, but its context, quality, and lifecycle.
By joining this team, you will be working in a high-impact environment, redefining the backbone of our metadata strategy. We don't just manage data, we build the maps that allow the entire enterprise to navigate it with confidence.
Responsibilities
- Scale & Optimize AI Agents: Extend metadata agents across new business domains using prompt design, RAG, and model fine-tuning.
- Evaluate & Improve: Build evaluation sets to measure agent performance on classification and quality checks. Analyze failures and design confidence scoring/fallback logic to ensure trustworthy outputs.
- Data & Integration: Collaborate with subject-matter experts to prepare annotated datasets. Integrate agent outputs into existing APIs, lightweight apps, and enterprise workflows.
- MLOps & Experimentation: Establish repeatable, tracked experiments with versioned datasets to ensure continuous, reproducible improvements.
- Agile Collaboration: Work in sprints alongside engineers and data stewards. Present methods, limitations, and demos to both technical and business stakeholders.
Who You Are
- Education: Advanced Bachelor’s or Master’s student in AI, Data Science, Computer Science, or a related field.
- Core Technical Skills: Strong Python proficiency with hands-on experience in PyTorch/TensorFlow, classic ML tooling, SQL, and Git/Docker/Linux. Experience building lightweight apps is a plus.
- AI/ML Experience: Practical knowledge of the full ML lifecycle. Preferred exposure to LLMs, agent frameworks, and reward design.
- Domain Interest: Familiarity with metadata, data catalogs, or lineage. A strong interest in ensuring data quality and traceability in regulated environments.
- Soft Skills: Clear written and spoken English, intellectual curiosity, persistence, and the ability to explain complex technical results to non-technical audiences.
Additional information
- Please include CV in English to your application.
- Due to regulatory requirements, non-EU/non-EFTA citizens must provide a certificate from their university confirming that the internship is mandatory and specifying its required duration, as well as a certificate of enrollment.
- You are currently enrolled at a university (Bachelor's or Master's degree), or you have completed your studies within the 12 months prior to the internship start date.
We are looking forward to your application!
Who we are
A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.
Let’s build a healthier future, together.
Roche is an Equal Opportunity Employer.
How we rate this
Internship - AI Metadata Augmentation at Roche rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI agents, Fine-tuning, and ML Ops. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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