Director, AI Engineering

About Us:

Structure Therapeutics develops lifechanging medicines for patients using advanced structurebased and computational drug discovery technology. The companys 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 clinicalstage 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.

Position Summary:

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.

Job Responsibilities:

Production AI Applications

• 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 productionize the solutions that demonstrate meaningful value.

AI Platform Engineering

• 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.

LLMOps MLOps and Software Delivery

• Implement the complete AI lifecycle from data ingestion and experimentation through validation deployment monitoring and retirement.
• Establish CI/CD processes for AI applications models prompts retrieval configurations and infrastructure.
• Build automated evaluations covering accuracy groundedness relevance safety latency cost and domain-specific performance.
• Create regression tests and release criteria for AI systems used in scientific or regulated workflows.
• Implement monitoring for model behavior data drift retrieval quality system performance and user feedback.
• Maintain versioning and traceability across training data models prompts code and evaluation results.
• Turn experimental notebooks and proofs of concept into tested documented observable and supportable production services.

Clinical and Scientific AI

• Partner with Clinical Development and Clinical Operations teams to automate labor-intensive elements of study design and study build.
• Develop systems that improve the quality and efficiency of clinical metadata edit checks test-data generation validation and study documentation.
• Build AI-assisted workflows for informed-consent content and other patient-facing or regulated documents.
• Develop document-classification and intelligent-filing capabilities for clinical and regulatory content.
• Collaborate with research teams on scientific knowledge retrieval computational modeling multimodal data imaging and biomarker-oriented applications.
• Apply AI to high-value workflows while preserving appropriate scientific and human oversight.

Responsible AI Security and Compliance

• Build access controls audit trails validation evidence and human-review mechanisms directly into AI systems.
• Partner with Quality Legal Compliance Security IT and Data teams to protect regulated data and proprietary scientific information.
• Support applicable GxP expectations including controlled change traceability reproducibility and auditability.
• Establish practical standards for model validation approved use monitoring documentation and incident escalation.
• Design controlled environments for evaluating models and vendors using representative production-relevant data.
• Ensure external services receive only authorized information and that sensitive data is handled according to company policy.

Hands-On Technical Leadership

• Lead and mentor AI and machine-learning engineers while remaining an active contributor to the production codebase.
• Participate directly in architecture design coding code review testing deployment and incident resolution.
• Establish engineering standards for maintainability documentation reproducibility security and operational ownership.
• Break ambiguous scientific and business problems into testable technical milestones.
• Build the team thoughtfully developing both AI depth and strong software-engineering practices.
• Create an environment in which engineers can experiment quickly while maintaining production and regulatory discipline.
• Communicate technical decisions clearly to scientists engineers product leaders and executives.

Technology and Vendor Evaluation

• Conduct hands-on evaluations of foundation models cloud AI services scientific software development frameworks and infrastructure providers.
• Build benchmarks based on realistic Structure workflows instead of relying primarily on vendor demonstrations.
• Compare alternatives based on output quality validation requirements security integration effort scalability latency cost and operational risk.
• Make pragmatic build-versus-buy recommendations and retain sufficient internal capability to avoid unnecessary vendor dependence.

Qualifications:

• BA/BS in Computer Engineering MBA a plus.
• Eight or more years of combined experience in AI machine learning software engineering data science or computational research.
• Demonstrated experience building AI or machine-learning capabilities that progressed from experimentation into production use.
• Current hands-on proficiency in Python and SQL including testing debugging profiling API development and code review.
• Experience building generative-AI or LLM applications using retrieval structured generation tool use fine-tuning or agentic workflows.
• Experience designing or operating a shared AI or machine-learning platform.
• Strong understanding of the complete ML lifecycle including data preparation experimentation validation deployment monitoring and retraining.
• Experience with cloud AI and data infrastructure on AWS Azure or Google Cloud.
• Knowledge of containerization distributed computing CI/CD model serving observability and production software practices.
• Experience working with cross-functional product and engineering teams to translate user needs into shipped capabilities.
• Demonstrated ability to lead and develop technical teams while remaining involved in implementation.
• Ability to explain complex technical decisions to scientific product business and executive audiences.
• An advanced degree in computer science engineering data science computational biology neuroscience bioinformatics or a related discipline - or equivalent professional experience.

Preferred Experience

• Building AI products for clinical development life sciences healthcare or another regulated industry.
• Clinical-study design or study-build automation.
• Electronic consent clinical document workflows regulated content management or intelligent document filing.
• Fine-tuning language models for domain-specific generation or validation tasks.
• AWS Bedrock or comparable managed foundation-model services.
• Machine-learning infrastructure MLOps data security and cost-efficient model serving.
• Computer vision scientific imaging multimodal analysis or computational modeling.
• Clinical-trial patient biomarker or other healthcare data.
• GCP GMP or 21 CFR Part 11 environments.
• Scientific publications patents open-source contributions or other evidence of technical depth.

Travel:

• Remote employees will be required to come into home office (South San Francisco) on a monthly basis. 10-20% business travel required (as needed)
• Remote employees will need to be present for monthly connection weeks company meetings and/or industry conferences.

Structure Therapeutics Inc. is an Equal-Opportunity Employer.

Structure Therapeutics is committed to fair and equitable compensation practices and we strive to provide employees with total compensation packages that are market competitive. For this role the anticipated base pay range is $248000-$310000. The exact base pay offered for this role will depend on various factors including but not limited to the candidates geography qualifications skills and experience.

At Structure base pay is only one part of your total compensation package. The successful candidate will be eligible for an annual performance incentive bonus new hire equity and ongoing performance-based equity. Structure Therapeutics also offers various benefits offerings including but not limited to medical dental and vision insurance 401k match unlimited PTO and a number of paid holidays including winter shutdown.

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Required Experience:

Director