Director, AI Engineering

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.

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.

Structure Therapeutics is seeking a Director of AI Engineering to build and scale production AI capabilities across drug discovery, clinical development, manufacturing, and business operations.

This is a hands‑on player‑coach position. Approximately 60-70% of the role will involve direct technical contribution: writing and reviewing code, developing AI applications, building model and data pipelines, creating evaluation frameworks, troubleshooting deployments, and converting prototypes into reliable production systems. The remaining time will focus on technical direction, team development, and close partnership with scientific, product, data, and business leaders.

The Director will lead a focused team while personally contributing to its most important systems. This role is intended for a leader who can move comfortably between AI strategy, system architecture, scientific problem-solving, and implementation.

• Personally design, code, test, and deploy production AI applications, services, APIs, and reusable engineering components.
• Build generative‑AI solutions for scientific search, clinical‑study design, document generation and classification, knowledge retrieval, workflow automation, and decision support.
• Develop retrieval‑augmented generation, semantic search, tool‑calling, structured‑generation, and agentic workflows.
• Fine‑tune or adapt language and machine‑learning models when general‑purpose models do not meet domain requirements.
• Build human‑in‑the‑loop review and validation workflows for scientific, clinical, and regulated use cases.
• Develop document‑intelligence capabilities that extract, classify, validate, generate, and route regulated content.
• Prototype directly with scientists and domain experts, then product ionize the solutions that demonstrate meaningful value.

• Build and evolve a centralized AI platform that enables teams across Structure to develop and deploy secure, reusable AI capabilities.
• Create shared services for model access, retrieval, prompt and workflow management, identity, authorization, observability, and evaluation.
• Design systems that support commercial foundation models, cloud AI services, specialized scientific models, and open‑source models.
• Build scalable model‑serving and data‑processing capabilities for language, document, imaging, and structured‑data applications.
• Develop production APIs and integration patterns that connect AI services with scientific, clinical, and enterprise systems.
• Optimize system performance, inference latency, reliability, and cost.
• Maintain clear abstraction layers so models and vendors can change without requiring complete application rewrites.

• Implement the complete AI lifecycle, from data ingestion and experimentation through validation, deployment, monitoring, and retirement.
• Establish CI/CD processes…