Applied AI Engineer

Join our fast-paced, innovative team as an Applied AI Engineer. In this role, you will work with a unique dataset that connects clinical data to trial data across 80+ trial sites. You will dig into the data to understand its structure and gaps, build systems that translate that understanding into real outcomes for sites and patients, and bring a point of view on how new capabilities apply to our specific problems. You will work hands-on with the data, build and ship models, LLM-based systems, and agents, and directly influence what we build and how. This role sits at the intersection of applied ML, engineering, and data science, and offers a unique opportunity to shape the future of clinical trials.

• Analyser les données cliniques et d'essai pour comprendre leur structure et leurs lacunes, et construire des systèmes qui traduisent cette compréhension en résultats concrets.
• Construire et expédier des modèles d'apprentissage automatique, des systèmes basés sur des LLM et des agents, de la phase de prototype à la production.
• Collaborer avec les équipes produit et ingénierie pour traduire les résultats en fonctionnalités expédiées.

• Experience with complex, messy, real-world data and how to make it useful without pretending it's clean
• Strong communication skills — able to convey findings clearly to technical and non-technical audiences and make a persuasive case for what to build next
• 5+ years of experience in applied AI/ML, software/ML engineering, or data science, with significant hands-on time building and shipping systems on real data
• Deep curiosity to understand data and connect it to the real world
• Strong statistical foundations and the judgment to know when a result is meaningful versus interesting
• Knowledgeable about ML capabilities and frameworks, with the judgment to know when something is genuinely applicable versus hype
• Fluent in Python and SQL, comfortable across the data stack from exploratory analysis through production-ready code and pipelines
• Experience working across data science, product, and engineering — able to build what's needed, not just analyze what exists
• Experience with healthcare data, clinical research, or life sciences
• Experience building and shipping production systems — including LLM-powered features (prompting, evaluation, RAG/agent pipelines) — not just research code or notebooks
• Experience building or defining the roadmap for an AI function from early stages
• Familiarity with clinical trial operations, disease classification, or patient identification problems
• Experience with LLM application frameworks (e.g. RAG, agent orchestration, eval harnesses)
• Experience working at growth-stage startups (strongly preferred)