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Bioinformatics Machine Learning information

What is a Bioinformatics Machine Learning job?

A Bioinformatics Machine Learning job involves applying machine learning techniques to analyze and interpret biological data, such as genomics, proteomics, and medical records. Professionals in this field develop algorithms, build predictive models, and enhance data-driven research in areas like personalized medicine and drug discovery. They work with large datasets, applying deep learning, neural networks, and other AI methods to extract meaningful insights. The role requires expertise in biology, statistics, and programming languages like Python or R.

What are the typical daily responsibilities for someone in a Bioinformatics Machine Learning position?

In a Bioinformatics Machine Learning role, your daily tasks usually involve developing and tuning machine learning models to analyze large biological datasets, such as genomics or proteomics data. You'll collaborate closely with researchers, biologists, and data scientists to understand project goals, interpret results, and refine analytical approaches. Routine work includes coding, troubleshooting algorithms, visualizing data outputs, and documenting findings for internal teams or publication. The role often requires balancing independent analysis with teamwork and regular communication across disciplines, making it both technically challenging and highly collaborative.

What are the key skills and qualifications needed to thrive in the Bioinformatics Machine Learning position, and why are they important?

A successful Bioinformatics Machine Learning professional needs a solid background in biology, statistics, and computer science, often backed by an advanced degree such as a Master's or PhD in bioinformatics, data science, or a related field. Proficiency with programming languages like Python or R, experience with machine learning libraries (e.g., TensorFlow, scikit-learn), and knowledge of version control systems are typical requirements, and relevant certifications can be beneficial. Strong problem-solving abilities, effective communication skills, and the capacity to work collaboratively in interdisciplinary teams set candidates apart. These skills are crucial for designing robust computational models, interpreting complex biological data, and translating findings into actionable insights in research or clinical settings.

What are the most commonly searched types of Bioinformatics Machine Learning jobs in Texas? The most popular types of Bioinformatics Machine Learning jobs in Texas are:
What are popular job titles related to Bioinformatics Machine Learning jobs in Texas? For Bioinformatics Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Bioinformatics Machine Learning jobs in Texas look for? The top searched job categories for Bioinformatics Machine Learning jobs in Texas are:
Bioinformatics Machine Learning jobs near you
Infographic showing various Bioinformatics Machine Learning job openings in Texas as of July 2026, with employment types broken down into 1% Locum Tenens, 51% As Needed, 31% Full Time, 4% Part Time, 11% Nights, and 2% Summer. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.
Sr. Bioinformatics ML/AI Engineer

Sr. Bioinformatics ML/AI Engineer

Baylor Genetics

Houston, TX • On-site

Full-time

Re-posted 23 days ago

Job description

Job Summary:
Baylor Genetics is a company specializing in genetic testing and bioinformatics solutions. They are seeking a seasoned machine learning and AI application engineer to develop and support their next-generation bioinformatics ML/AI platform, focusing on genomic and clinical data modeling and application development.
Responsibilities:
• Serves as the SME in Bioinformatics ML/AI application development in a clinical genetic testing setting. Provides hands-on support towards building the 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
Qualifications:
Required:
• 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.
• 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 languages, 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 NoSQL
• Familiarity with advanced data visualization techniques
• Strong curiosity and the ability to learn quickly and adapt to a fast-changing environment
Preferred:
• DevOps experience such as unit testing, CI/CD is a plus.
Company:
Baylor Genetics offers a full spectrum of cost-effective, genetic testing, and provides clinically relevant solutions. Founded in 1978, the company is headquartered in Houston, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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