Machine Learning Engineer
Position Overview:
- We are seeking an experienced contractor with expertise in AI rapid prototyping, predictive modeling, and machine learning to support the DAISAE platform and predictive analytics subteams within the RDE 9 program.
- This role focuses on developing shared data and technical foundations, including ontologies and knowledge graphs, prototyping agentic AI capabilities, and advancing predictive models to support drug safety risk prediction throughout the product lifecycle.
- Key ResponsibilitiesAI Prototyping & MCP Tool Development
- Explore, evaluate, and rapidly prototype emerging AI tools and agentic AI capabilities
- Develop Model Context Protocol (MCP) tools and AI agent integrations for the DAISAE platform
- Predictive Modeling & Analytics
- Build and refine predictive models using multimodal datasets, including in vitro and in vivo data, omics, clinical trial data, real-world data (RWD), and spontaneous safety reports
- Support drug safety risk prediction, from early molecule-level evaluation through late-stage development and post-marketing patient safety
- Architecture & Platform Design
- Translate feasibility assessments and prototype evaluations into architectural designs and platform requirements
- Contribute to shared data and technical foundations, including ontologies and knowledge graphs
- Collaboration & Technical Governance
- Develop reusable technical capabilities through agile prototyping before transitioning components to Roche Digital Tech (RDT) for formal validation, compliance, and production rollout
- Collaborate with data scientists, safety scientists, and RDT technical partners to align technical execution with business priorities
- Required Qualifications & Experience
- EducationMSc or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or a related quantitative field
- Technical Expertise
- Strong expertise in predictive modeling, machine learning, and AI prototyping
- Hands-on experience developing agentic AI systems, large language model (LLM) tools, and MCP tools
- Proficiency in working with biomedical datasets, such as omics, clinical trial data, real-world data, or in vitro/in vivo data
- Strong software engineering skills tailored to data science, with an agile prototyping mindset
- Domain Knowledge
- Experience in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable
- Personal Attributes
- An AI-native approach to problem-solving and the ability to adapt to rapidly evolving AI frameworks
- Proactive mindset and strong communication skills for working in global, cross-functional teams