Teams at AQEMIA

Own end-to-end scientific data pipelines from ingestion through trusted, model-ready datasets. Model compounds, structures, assays, predictions, and experimental results while ensuring data quality, lineage, governance, and observability. Partner with ML engineers, data scientists, and researchers to build scalable solutions, shape data architecture, develop self-service and API capabilities, and mentor junior engineers.

Operate and evolve AQEMIA’s AWS cloud platform using infrastructure as code, Kubernetes, and GitOps. Own observability, incident response, security, cost management, CI/CD, internal tooling, and platform reliability. Plan GPU capacity for scientific workloads, improve developer experience, and contribute to architecture and long-term platform strategy.

Lead biology strategy across early-stage small-molecule drug discovery programs, including target identification and validation, screening cascade design, compound evaluation, and progression toward proof of concept. Partner with multidisciplinary chemistry, modeling, platform, and program teams; manage external CROs; guide experimental strategy; and support data-driven portfolio prioritization, particularly in oncology and immuno-oncology.