Machine Learning Engineer – Predictive Modeling & Generative AI
Job Title: Machine Learning Engineer – Predictive Modeling & Generative AI
Pay Rate: $74/hr on W2Location : San Francisco, CA ( 100% Remote )
Duration: 14 Month Contract with a possible extension
Position Summary:
We are seeking a contractor with a background combining AI rapid prototyping and Predictive Modeling to support the DAISAE platform and predictive analytics sub teams within the RDE 9 program. This role focuses on co-developing shared data and technical foundations (e.g. Ontologies, Knowledge Graphs), prototyping agentic AI capabilities, and advancing predictive models for drug safety risk prediction across the product lifecycle.
Key Responsibilities:
- AI Prototyping & MCP Tool Development: Rapidly explore, evaluate, and prototype emerging AI tools and agentic capabilities, specifically developing MCP tools and AI agent integrations for DAISAE.
- Predictive Modeling: Build and refine predictive models using multi-modal datasets (including in vitro, in vivo, omics, clinical trial data, real-world data (RWD), and spontaneous safety reports) to enable risk prediction from early molecule-level evaluation to late-stage and post-marketing patient safety.
- Architecture & Platform Design: Translate feasibility findings and prototype evaluations into concrete architectural design and platform requirements for shared data and technical foundations.
- Co-Development & Governance Collaboration: Work in an agile way to prototype reusable technical capabilities before transitioning components to RDT (Roche Digital Tech) for formal validation, compliance, and production rollout.
- Cross-Functional Alignment: Collaborate with data scientists, safety scientists, and RDT technical partners to ensure technical execution aligns with business priorities. Qualifications & Experience
Education:
MSc 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, LLM tools, and MCP tools.
- Proficiency in handling biomedical datasets (omics, clinical trials, RWD, or in vitro/in vivo data).
- Agile prototyping mindset with software engineering skills tailored for data science. Domain Knowledge:
- Background in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable.