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Bioinformatics Machine Learning Internship Jobs in Texas

Mariana Minerals

Machine Learning Engineer

Houston, TX · On-site

Required : • 0-4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing--or a strong recent graduate with demonstrated project ...

New

Avride

Machine Learning Engineer Internship

Austin, TX · On-site

About the Internship At Avride, ML Engineer Interns operate at the intersection of cutting-edge ... Machine Learning / Math Foundation: Strong understanding of deep learning, reinforcement learning ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98K - $130K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Baylor College of Medicine

Intern/Aide

Houston, TX

$14.25 - $19/hr

... machine learning approaches * Visualizing and presenting research results for scientific interpretation This opportunity is ideal for individuals interested in bioinformatics, data science, and ...

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Bioinformatics Machine Learning Internship information

What is a Bioinformatics Machine Learning Internship?

A Bioinformatics Machine Learning Internship is a temporary position, usually for students or recent graduates, where interns gain hands-on experience applying machine learning techniques to biological data. Interns may work on projects like analyzing genomic sequences, predicting protein structure, or developing algorithms for biomedical research. The role involves coding, data analysis, and collaborating with scientists to solve real-world biological problems. It offers exposure to both computational methods and biological sciences, preparing interns for careers in bioinformatics, data science, or research.

What are the key skills and qualifications needed to thrive as a Bioinformatics Machine Learning Intern, and why are they important?

To thrive as a Bioinformatics Machine Learning Intern, you need a solid background in biology, statistics, and computer science, typically supported by relevant coursework or a degree in bioinformatics, computational biology, or a related field. Familiarity with programming languages like Python or R, experience using bioinformatics tools (e.g., BLAST, Bioconductor), and knowledge of machine learning frameworks such as TensorFlow or scikit-learn are highly valued. Attention to detail, problem-solving skills, and effective communication help interns collaborate on interdisciplinary teams and interpret complex datasets. These skills ensure interns can contribute meaningfully to research projects, derive insights from biological data, and communicate findings clearly.

What are some typical projects or tasks a Bioinformatics Machine Learning Intern might work on during their internship?

As a Bioinformatics Machine Learning Intern, you'll often contribute to projects that involve developing and testing algorithms for analyzing biological data, such as genomic sequences or protein structures. Typical tasks may include preprocessing large datasets, implementing machine learning models to identify patterns or make predictions, and visualizing results for team discussions. Interns frequently collaborate with both computational scientists and experimental biologists, gaining exposure to interdisciplinary teamwork and real-world applications. This hands-on experience helps interns build both technical and domain-specific skills, preparing them for advanced roles in bioinformatics or data science.

What is the difference between Bioinformatics Machine Learning Internship vs Bioinformatics Data Analyst Internship?

AspectBioinformatics Machine Learning InternshipBioinformatics Data Analyst Internship
Required SkillsProgramming, machine learning, bioinformatics toolsData analysis, statistical skills, bioinformatics tools
Work EnvironmentResearch labs, biotech companies, academic institutionsResearch labs, healthcare, biotech firms
Industry UsageDeveloping algorithms, predictive models in bioinformaticsAnalyzing biological data, generating reports

While both internships involve bioinformatics, the Bioinformatics Machine Learning Internship focuses on developing machine learning models and algorithms, whereas the Bioinformatics Data Analyst Internship emphasizes analyzing biological data and generating insights. Both roles require programming and bioinformatics skills but differ in their core focus and application.

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:
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Bioinformatics Machine Learning Internship jobs near you
Sr. Bioinformatics ML/AI Engineer

Sr. Bioinformatics ML/AI Engineer

Baylor Genetics

Houston, TX • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

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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