Job Description
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Company Overview
The organization develops life-changing medicines for patients using advanced structure-based and computational drug discovery technology. Its platform combines molecular-interaction visualization, computational chemistry, and data integration to design orally available small-molecule medicines that address limitations associated with biologic and peptide drugs.
Job Responsibilities
Production AI Applications
- 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 solutions that demonstrate meaningful value.
AI Platform Engineering
- Build and evolve a centralized AI platform that enables teams across the organization 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 supporting 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 connecting 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.
Qualifications
- BA/BS in Computer Engineering; MBA is 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.
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