Sr. Bioinformatics ML/AI Engineer

Summary

A seasoned machine learning and AI application engineer with strong knowledge and hands-on experience on genomic/clinical data modeling and machine learning, AI/GenAI application development, data engineering, and cloud computing.

Qualifications

• Education:
• Master's or higher degree in Computer Engineering, Data Science, Bioinformatics, Machine Learning and Data modeling, or related field with five (5) years of experience in genomic data analysis and application development.
• Experience:
• Hands-on experience in clinical bioinformatics pipeline development including secondary/tertiary analysis, variant interpretation and classification pipeline R&D, and automated report generation
• 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 (such as Huggingface, TensorFlow)
• Hands-on experience in automated and scalable AI/GenAI application evaluation, development, and deployment.
• Hands-on experience in RAG AI framework
• Hands-on experience with scripting language, such as Bash and Python
• Strong experience in cloud platform (Azure) and data services (data lakehouse/data warehouse)
• Experience in context-aware OCR
• Experience in databases, including SQL and no-SQL
• Familiarity with advanced data visualization techniques
• 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
  
  

Duties And Responsibilities

• Serves as the SME in Bioinformatics ML/AI application development in a clinical genetic testing setting. Provides hands-on support towards building company’s next-generation bioinformatics ML/AI platform
• Designs, develops, evaluates, and deploys state-of-the-art ML/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 enhance the overall performance of the genetic testing workflow
• Supports both internal and external data requirements by leveraging AI/ML 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
  
  

Physical Demands And Work Environment

• Frequently required to sit
• Frequently required to stand
• Frequently required to utilize hand and finger dexterity
• Frequently required to talk or hear
• Frequently required to utilize visual acuity to operate equipment, read technical information, and/or use a keyboard
• Occasionally exposed to bloodborne and airborne pathogens or infectious materials
  
  

EEO Statement

Baylor Genetics is proud to be an equal opportunity employer dedicated to building an inclusive and diverse workforce. We do not discriminate based on race, religion, color, national origin, sex, sexual orientation, age, gender identity, veteran status, disability, genetic information, pregnancy, childbirth, or related medical conditions, or any other status protected under applicable federal, state, or local law.