Senior AI Engineer

We are partnering with a company that is the scientific intelligence layer powering life sciences dealmaking. We combine expert-developed diligence frameworks, AI reasoning trained on drug development, and a secure, unified evidence base to help pharma, biotech, and deal teams move assets from evaluation to term sheet — faster and with more confidence. The Role We are looking for an AI Engineer to build the reasoning systems at the core of our platform: AI agents that understand drug development risk, therapeutic-area- and modality-specific models, and end-to-end pipelines that turn fragmented scientific evidence into decision-ready outputs. You'll work at the intersection of applied AI and life sciences, building systems that require both technical rigor and grounding in real scientific evidence — with human-in-the-loop validation and full data provenance baked in from day one. ***This is a hybrid role with 2 days a week onsite in the Cambridge office*** What You'll Do Design, build, and ship AI agents and reasoning pipelines that evaluate biological mechanism validity, translational/regulatory/clinical risk, and deal-relevant scientific evidence Develop and fine-tune models trained across therapeutic areas, modalities, and the drug development lifecycle Build retrieval and evidence-grounding systems that connect proprietary, public, and internal data sources into a single, auditable evidence base Implement human-in-the-loop validation workflows and maintain end-to-end data provenance across every model output Partner closely with scientific and product teams to translate expert diligence frameworks (PTRS, NPV, development scenario modeling) into production AI systems Own performance, reliability, and evaluation of models and agents in production, iterating quickly based on real deal usage Work within a security-first architecture (single-tenant, SSO, encryption at rest/in transit, ISO/IEC 42001:2023-aligned) — your data and model choices will need to hold up to pharma-grade scrutiny What We're Looking For Strong software engineering fundamentals, with production experience building and deploying AI/ML systems (not just notebooks/prototypes) Hands-on experience with LLM-based systems — agent architectures, RAG/retrieval pipelines, evaluation frameworks, prompt/model tuning Comfort working with ambiguous, evidence-heavy, semi-structured data and translating expert domain frameworks into reliable systems A bias toward shipping — comfortable in a small, fast-moving team where you own problems end-to-end Genuine interest in life sciences, drug development, or healthcare AI (prior experience in the space is a plus, not a requirement) Excellent judgment around data provenance, auditability, and security — this product handles some of the most sensitive data in the industry Nice to Have Experience with biomedical/scientific NLP, clinical trial data, or drug development datasets Background in a regulated or high-trust data environment (healthcare, fintech, etc.) Experience building agentic systems with tool use / multi-step reasoning Show more Show less