Machine Learning Engineer
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At CluePoints, we’re redefining how clinical trials are run. As the premier provider of Risk-Based Quality Management (RBQM) and Data Quality Oversight software, we harness advanced statistics, artificial intelligence, and machine learning to ensure the quality, accuracy, and integrity of clinical trial data, helping life sciences organizations bring safer, more effective treatments to patients faster.
We’re proud to be an ambitious, fast-growing technology scale-up with a dynamic and diverse international team representing more than 40 nationalities. Collaboration, flexibility, and continuous learning are part of our DNA.
At CluePoints, you’ll find a culture where you can grow, make an impact, and have fun along the way. Guided by our values of Care, Passion, and Smart Disruption, we’re united by a shared mission: to create smarter ways to run efficient clinical trials and deliver AI-powered insights that improve human outcomes worldwide.
We are looking for an AI Engineer to join our AI Innovation team, focused on exploring, prototyping, and operationalizing cutting-edge AI solutions for clinical data review. This role sits at the intersection of research and engineering: you will investigate emerging AI capabilities—particularly LLMs and agentic AI systems—and translate them into impactful use cases within clinical trials. While the primary focus is on innovation and experimentation, you are expected to drive selected prototypes through to robust, production-ready solutions.
Main Qualifications
- Master’s or PhD in Computer Science, AI, Machine Learning, or a related quantitative field
- Strong programming skills in Python
- Proven experience working with LLMs and modern AI frameworks (e.g., OpenAI, Anthropic, Mistral, Meta)
- Solid understanding of agentic AI concepts and orchestration frameworks (e.g.,
- LangGraph, AutoGen, CrewAI, function calling)
- Experience with Retrieval-Augmented Generation (RAG) and/or hybrid AI systems
- Strong software engineering fundamentals (modular design, APIs, version control with Git)
- Experience with clinical trial data (protocols, EDC, CTMS, AE/SAE reporting)
- Familiarity with regulated environments (e.g., life sciences, healthcare)
- Experience deploying AI/ML systems into production environments
- Knowledge of vector databases, embeddings, and information retrieval systems
- Background in NLP, deep learning, or applied AI research
- Experience with data quality, statistical methods, or risk-based monitoring
- Familiarity with GPU computing and performance optimization
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