Senior Director, AI & Data Engineering

Structure Therapeutics•South San Francisco, CA

4d•$265,000 - $334,000•Onsite

About The Position

Structure Therapeutics develops life‐changing medicines for patients using advanced structure-based and computational drug discovery technology. The company’s platform combines the latest advancements in visualization of molecular interactions, computational chemistry, and data integration to design orally available, superior small molecule medicines that overcome current limitations of biologic and peptide drugs. We are advancing a clinical‐stage pipeline of differentiated treatments focused on chronic diseases with high unmet need, including cardiovascular, metabolic, and pulmonary conditions. Structure Therapeutics is led by an experienced group of international drug innovators and financed by top-tier global life sciences investors. The company completed an initial public offering (IPO) in February 2023. With offices in California and Shanghai, Structure Therapeutics has the benefit of being at the center of life science innovation in both the US and China and capitalizing on the strengths of each geographic location. Position Summary: We are seeking a Senior Director, AI & Data Engineering to lead our enterprise-wide AI/ML strategy, data platform architecture, and engineering capabilities. This strategic leadership role will partner closely with Discovery, Preclinical, Clinical, and G&A organizations across Structure Therapeutics, enabling the design, implementation, and scaling of AI/ML solutions that are powered by robust, compliant, and scalable data infrastructure aligned with our corporate goals. You will architect and execute a forward-looking AI-first strategy while owning the underlying data platforms that make AI/ML initiatives successful. This role serves as the primary interface between business leaders and our AI/ML + data teams (internal and external), ensuring initiatives deliver measurable scientific innovation, operational efficiency, and regulatory compliance in a biopharma environment.

Requirements

BS degree (MS/PhD preferred) in Computer Science, Data Science, Engineering, Bioinformatics, Life Sciences, or related field.
12+ years of experience in IT, Data Science, Data Engineering, AI/ML, Digital Transformation, or related disciplines, with at least 5+ years leading AI/ML initiatives and enterprise-scale data platforms at scale in biotech/pharma or highly regulated industries.
Demonstrated ability to translate complex AI/ML technologies and data architectures into actionable business and scientific solutions.
Strong knowledge of biopharma AI/ML use cases (target discovery, patient recruitment, predictive analytics, NLP for regulatory/safety, translational research) and supporting data ecosystems (clinical, real-world, omics data).
Hands-on expertise with modern data architectures (lakes, warehouses, lakehouse/mesh), ETL/ELT, streaming, data modeling, governance, MDM, and cloud platforms (AWS, GCP, Azure, Snowflake, Databricks).
Deep expertise in data science, machine learning, statistical modeling, modern ML frameworks (PyTorch, TensorFlow), MLOps tools, and generative/agentic AI technologies.
Proven track record of driving AI/ML innovation from concept to scaled deployment, including building/governing ethical AI frameworks and navigating compliance in regulated environments.
Experience managing external vendors, AI partners, cloud-based platforms, and working with product management teams in an agile environment.
Strong business acumen with the ability to align AI/ML and data initiatives to strategic and operational goals.
Advanced knowledge of workflow tools, automation systems, and compliance frameworks (GxP, data integrity, auditability).
Prior experience building internal AI Centers of Excellence, AI operating models, or enterprise data platforms in biopharma.
Understanding of data governance, FAIR principles, and life sciences data ecosystems (clinical, real-world, omics).

Responsibilities

Enterprise AI & Data Strategy & Leadership
Develop and continuously refine Structure’s enterprise AI/ML strategy, integrated with a comprehensive data platform strategy (data lakes/warehouses, lakehouse/mesh architectures, master data, and analytics infrastructure) aligned with corporate goals, scientific priorities, and operational needs.
Identify, prioritize, and champion high-impact AI/ML use cases across Preclinical, Clinical Development, Regulatory, Safety, and G&A functions, while ensuring foundational data readiness and governance.
Lead modernization of data platforms to support AI/ML lifecycle needs (feature stores, model pipelines, MLOps, real-time ingestion, and predictive analytics).
Act as a thought leader on emerging AI/ML technologies, generative and agentic AI, industry trends, regulatory developments (FDA/EMA AI/ML guidelines), and ethical AI practices.
Business Partnership & Stakeholder Engagement
Serve as the strategic business partner and advisor to senior executives across R&D, Clinical, and G&A teams on AI/ML opportunities, data architecture roadmaps, and alignment.
Collaborate with cross-functional teams to understand business and scientific challenges, define AI-driven solutions supported by modern data engineering, and establish KPIs for success.
Communicate complex AI/ML and data architecture concepts in business-friendly terms to drive alignment and adoption across non-technical stakeholders.
AI/ML Solution Delivery, Data Engineering & Program Management
Lead the planning, delivery, and change management of AI/ML programs and data platform initiatives across the enterprise.
Architect scalable, secure, high-performance data infrastructures (ETL/ELT pipelines, streaming ingestion, data modeling, semantic layers, cataloging, and lineage) for structured and unstructured biopharma data (omics, clinical, real-world evidence).
Partner with IT, Data Science, Bioinformatics, Clinical Operations, and external AI/data solution providers to ensure timely execution and operational excellence (CI/CD, observability, SLAs, incident management).
Oversee data governance, FAIR principles, data readiness, risk management, and compliance (GxP, 21 CFR Part 11, HIPAA, GDPR) for all AI/ML initiatives.
AI Operating Model, Data Governance & Compliance
Design and operationalize a centralized AI operating model with clear accountability, delivery mechanisms, and performance monitoring, underpinned by enterprise data architecture.
Implement frameworks for AI ethics, bias detection, model governance, data privacy, quality, and integrity in highly regulated biopharma environments.
Enable self-service analytics, data democratization, and seamless interoperability to accelerate AI-enabled drug discovery, translational research, and intelligent automation.
Leadership & Team Development
Build, mentor, and inspire high-performing teams of AI/ML engineers, data architects, data engineers, prompt engineers and operations professionals.
Foster a culture of innovation, continuous learning, data-driven decision-making, quality, and cross-functional collaboration in a matrixed organization.
Influence senior executives, drive alignment through ambiguity, and manage external vendors, AI partners, and cloud-based platforms.