Roche
Healthcare
Internship:AIInfrastructureandMLOpsModernizationthroughVibeOps(pRED)
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“Internship: AI Infrastructure and MLOps Modernization through VibeOps (pRED) at Roche. Skills: AI Infrastructure, MLOps, VibeOps, Python. Research VibeOps workflows in MLOps. Prototype small-scale solutions”
What You'll Achieve.
Advance AI, data, and computational sciences; Modernize computational and data ecosystems; Drive AI adoption; Build and deploy AI solutions; Optimize workflows; Scale model training and inference; Create applications; Accelerate drug discovery; Make AI/ML an everyday utility; Enable large-scale, reliable AI/ML solutions; Provide a state-of-the-art platform; Support secure, scalable self-service adoption; Enhance the MLOps lifecycle; Improve model training/inference speed; Improve deployment reliability; Improve resource efficiency; Translate research into practical solutions
Industry & Context.
Problem-solving skills; Tackling complex challenges
Non-EU/EFTA citizens must provide a certificate from the university stating that an internship is mandatory
What They're Looking For.
Must Have
Master student or Bachelor/Master graduate within 12 months in Computer Science, Biotechnology or related field, Experience in Python, Experience with scripting/automation (e.g. Bash), Basic understanding of LLMs and Generative AI concepts, Interest in Machine Learning Operations, Interest in DevOps principles, Foundational knowledge of cloud platforms (AWS, Azure, or GCP), Foundational knowledge of containerization technologies (e.g. Docker, Kubernetes)
Nice to Have
Some experience with MLOps platforms/tools (e.g., MLflow, Kubeflow, SageMaker), Some familiarity with CI/CD pipelines (e.g., GitLab CI, GitHub, Jenkins), Interest or exposure to Agentic AI concepts, Some understanding of distributed systems, Some understanding of parallel computing frameworks
What You'll Do.
Research VibeOps workflows in MLOps
Prototype small-scale solutions
Propose integration steps
Assist in developing tools
Assist in documenting pipelines
Assist in developing frameworks
Contribute to implementation
Contribute to optimization
Work with ML Engineers
Work with Data Scientists
Work with IT specialists
How You'll Work.
Team & Collaboration
Work closely with ML Engineers; Work closely with Data Scientists; Work closely with IT infrastructure specialists; Efficient cross-functional collaboration
Full Job Description
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 Advances in AI, data, and computational sciences are transforming drug discovery. The new Computational Sciences Center of Excellence (CoE) unifies Genentech (gRED) and Pharma (pRED) efforts to leverage data and AI for innovative medicines. Within CoE, the Data and Digital Catalyst (DDC) modernizes computational and data ecosystems. The Engineering - AI Enablement group, within DDC, drives AI adoption, builds and deploys AI solutions to optimize workflows, scales model training and inference, and creates applications to accelerate drug discovery. They aim to make AI/ML an everyday utility for tasks from data analysis to documentation. The pRED-MLOps team within the AI Enablement group enables large-scale, reliable AI/ML solutions across the early development pipeline, providing a state-of-the-art platform for developing, deploying, and monitoring high-impact models to accelerate drug discovery. The team is cross-functional, impact driven, independent, and constantly evolving to meet the scientific needs. ### The Opportunity * **VibeOps Research and Prototyping:** Research best practices, tools, and methodologies for applying VibeOps workflows in MLOps environments, with a focus on productivity, efficient cross-functional collaboration, and secure self-service, as well as an emphasis on understanding risks, limitations, and trade-offs * Prototype small-scale solutions, focusing on VibeOps concepts and best practices, tools, and methodologies, within realistic MLOps contexts. The goal is experim
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