Lead Bioinformatics AI Scientist

About the role

The Lead Bioinformatics AI Scientist will serve as a scientific and technical leader, driving the design, development, and implementation of advanced AI methods, algorithms, and workflows to enhance Baylor Genetics' clinical testing and genomic data analysis capabilities.

Responsibilities

• Serves as the visionary leader in Bioinformatics AI application development in a clinical genetic testing setting.
• Provides technical guidance and hands-on support towards building company’s next-generation bioinformatics AI platform.
• Identifies, prototypes, and develops state-of-the-art AI applications to revolutionize clinical testing and genomic analysis workflow.
• Designs, develops, evaluates, and deploys novel AI solutions to gain valuable data insights based on the genetical, phenotypical, and clinical datasets.
• Evaluates, adopts, and customizes GenAI models based on both internal and external datasets to build next-generation clinical genetic testing platforms.
• Supports both internal and external data requirements by leveraging AI and GenAI capabilities to keep up with the increasing demands of the business.
• Collaborates in a multidisciplinary and regulated clinical diagnostics environment with geneticists, bioinformaticians, software engineers, and IT infrastructure professionals.

Requirements

• Education: Master's or higher degree (PhD preferred) in Bioinformatics, Machine Learning and AI, Computer Science, Data Science or related quantitative field.
• Experience: 8+ years of professional experience in bioinformatics, AI application development, machine learning and/or genomic data analysis, including 3–5 years in a principal or leadership role.
• Hands-on experience in state-of-the-art GenAI application development, LLM model turning, agentic AI, and model context protocol (MCP).
• Hands-on experience in building and/or adopting novel AI and GenAI solutions for business specific applications, especially in the field of clinical testing and genomic data analysis.
• Hands-on experience in automated and scalable AI/GenAI application evaluation, development, and deployment in the production environment requiring fast turn-around-time (TAT) and high reliability.
• Hands-on experience in human genetics/multi-omics data modeling and application development especially in next-generation sequencing data.
• Hands-on experience in machine learning framework (Huggingface, TensorFlow, PyTorch, etc.).
• Hands-on experience with scripting language, such as Bash and Python.
• Strong experience in cloud platform (Azure, AWS, GCP) and data services (data lakehouse/data warehouse).
• Experience in context-aware OCR.
• Experience in databases, including SQL and no-SQL.
• DevOps experience such as unit testing, CI/CD is a plus.
• Strong curiosity and the ability to learn quickly and adapt to a fast-changing environment.

Core Competencies

• Strong scientific reasoning and analytical problem-solving skills.
• Proven ability to lead R&D initiatives from concept through validation and deployment.
• Deep understanding of genomic data, AI applications, and biological context.
• Excellent written and verbal communication for technical and clinical translation.
• Collaborative mindset and ability to work across disciplines.
• Commitment to innovation, quality, and patient-centered outcomes.