STEM OPT Machine Learning Intern Jobs
Machine Learning Intern roles qualify for the STEM OPT 24-month extension when your degree falls under an eligible CIP code and your employer is enrolled in E-Verify. With up to 36 months of total OPT work authorization, you have real runway to build ML experience while staying compliant with USCIS training plan requirements.
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Get Access To All JobsINTRODUCTION
Bioinformatics Machine Learning Intern
RefinedScience | United States (hybrid or remote)
At RefinedScience, our mission is to advance care by bringing together the best science, data and minds – disease by disease, patient by patient, cell by cell to discover pathways to life beyond disease.
ROLE
We are seeking a highly motivated Bioinformatics Machine Learning Intern to join our team. This internship is designed for Ph.D. candidates with experience applying machine learning, deep learning, or generative AI methods to single-cell omics data. You will contribute to active projects spanning single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development.
Our Bioinformatics team plays a crucial role in integrating computational biology, large-scale data analysis, and machine learning to drive discoveries in precision medicine and drug development.
KEY ACTIVITIES
- Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
- Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
- Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
- Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
- Document and optimize computational workflows following reproducible research best practices
- Present findings through technical reports, visualizations, and presentations to cross-functional teams
BASIC QUALIFICATIONS
- Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
- Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
- Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
- Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
- Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
- Strong problem-solving skills and ability to communicate complex insights effectively
PREFERRED QUALIFICATIONS
Machine Learning & AI
- Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
- Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
- Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
- Exposure to representation learning, variational autoencoders, or contrastive learning methods
- Familiarity with scikit-learn, XGBoost, or similar ML libraries
- Interest in or experience with LLMs, RAG systems, or agentic AI tooling
Bioinformatics
- Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
- Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
- Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)
- Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)
Engineering & Infrastructure
- Familiarity with Linux/Unix CLI and version control (Git/GitHub)
- Experience with containerization (Docker, Singularity) and environment management (conda, venv)
- Exposure to cloud computing platforms (GCP preferred)
- Familiarity with workflow managers (Nextflow, Snakemake)
- Adherence to best-practices for conduct reproducible computational research
DURATION
8–10 weeks
WHY YOU'LL LOVE REFINEDSCIENCE
Team + Values
At RefinedScience, we seamlessly integrate top-tier clinical and biological data with expert knowledge to provide unparalleled insights. We maximize patient impact with these unique insights by optimizing clinical trial probability of success and time to actionable results. We work across biopharma and we are a trusted partner in achieving better results, faster – working together to unlock strategic advantage.
Our Values
- Act with Purpose – We believe in rigor through deliberate and thoughtful actions
- Be Curious – Curiosity is the spark that ignites innovation and growth
- Take Ownership – True ownership leads to pride and commitment in the work we do
- Invest in Relationships – Building strong connections is the foundation for effective collaboration and trust for long term success
- Embrace Agility – We celebrate agile thinking, resilience, and adaptability
COMPENSATION
- $34-$38 per hour
INTRODUCTION
Bioinformatics Machine Learning Intern
RefinedScience | United States (hybrid or remote)
At RefinedScience, our mission is to advance care by bringing together the best science, data and minds – disease by disease, patient by patient, cell by cell to discover pathways to life beyond disease.
ROLE
We are seeking a highly motivated Bioinformatics Machine Learning Intern to join our team. This internship is designed for Ph.D. candidates with experience applying machine learning, deep learning, or generative AI methods to single-cell omics data. You will contribute to active projects spanning single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development.
Our Bioinformatics team plays a crucial role in integrating computational biology, large-scale data analysis, and machine learning to drive discoveries in precision medicine and drug development.
KEY ACTIVITIES
- Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
- Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
- Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
- Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
- Document and optimize computational workflows following reproducible research best practices
- Present findings through technical reports, visualizations, and presentations to cross-functional teams
BASIC QUALIFICATIONS
- Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
- Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
- Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
- Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
- Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
- Strong problem-solving skills and ability to communicate complex insights effectively
PREFERRED QUALIFICATIONS
Machine Learning & AI
- Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
- Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
- Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
- Exposure to representation learning, variational autoencoders, or contrastive learning methods
- Familiarity with scikit-learn, XGBoost, or similar ML libraries
- Interest in or experience with LLMs, RAG systems, or agentic AI tooling
Bioinformatics
- Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
- Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
- Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)
- Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)
Engineering & Infrastructure
- Familiarity with Linux/Unix CLI and version control (Git/GitHub)
- Experience with containerization (Docker, Singularity) and environment management (conda, venv)
- Exposure to cloud computing platforms (GCP preferred)
- Familiarity with workflow managers (Nextflow, Snakemake)
- Adherence to best-practices for conduct reproducible computational research
DURATION
8–10 weeks
WHY YOU'LL LOVE REFINEDSCIENCE
Team + Values
At RefinedScience, we seamlessly integrate top-tier clinical and biological data with expert knowledge to provide unparalleled insights. We maximize patient impact with these unique insights by optimizing clinical trial probability of success and time to actionable results. We work across biopharma and we are a trusted partner in achieving better results, faster – working together to unlock strategic advantage.
Our Values
- Act with Purpose – We believe in rigor through deliberate and thoughtful actions
- Be Curious – Curiosity is the spark that ignites innovation and growth
- Take Ownership – True ownership leads to pride and commitment in the work we do
- Invest in Relationships – Building strong connections is the foundation for effective collaboration and trust for long term success
- Embrace Agility – We celebrate agile thinking, resilience, and adaptability
COMPENSATION
- $34-$38 per hour
See all 119+ STEM OPT Machine Learning Intern Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Machine Learning Intern
Verify your CIP code before applying
Check your degree's CIP code against the official STEM Designated Degree Program List to confirm STEM OPT eligibility. Computer science, statistics, and electrical engineering codes commonly support ML intern roles, but data science program codes vary by institution.
Confirm E-Verify enrollment before accepting offers
Search the E-Verify employer database to confirm a company is actively enrolled before signing an offer. Startups and research labs frequently hire ML interns but may not yet be enrolled, which disqualifies them as STEM OPT employers.
Draft your I-983 around measurable ML deliverables
Your I-983 training plan must link specific learning objectives to your ML intern work. Frame deliverables around model evaluation metrics, dataset pipelines, or deployment tasks rather than vague project descriptions that DSOs commonly flag for revision.
Target employers with established research infrastructure
Use Migrate Mate to filter for employers with active STEM OPT hiring history in machine learning and data science roles. Companies running internal research teams or university partnerships are structured to support the E-Verify and I-983 compliance process.
Apply early if your OPT end date overlaps cap season
If your initial OPT expires between April and September, cap-gap protection can extend your authorization through September 30 while an H-1B visa petition is pending. Submit your STEM OPT extension application to USCIS at least 90 days before your EAD expires.
Use O*NET to align your training plan job zone
Look up the Machine Learning Engineer or Data Scientist occupation in O*NET to identify the job zone and typical education requirements. Use that language in your I-983 to demonstrate the role requires your STEM degree rather than general technical skills.
Frequently Asked Questions
Does a Machine Learning Intern role qualify for the STEM OPT extension?
It qualifies if your degree is on the STEM Designated Degree Program List and your employer is enrolled in E-Verify. Machine learning intern positions typically require a background in computer science, statistics, applied mathematics, or a related STEM field. Your DSO confirms eligibility based on your specific CIP code, not the job title alone.
What does an employer need to do to hire you on STEM OPT?
The employer must be enrolled in E-Verify and co-sign your I-983 training plan before your STEM OPT extension begins. The I-983 requires them to describe your learning objectives, supervision structure, and how the role relates to your STEM degree. Employers who haven't hired STEM OPT students before often need guidance on this step.
How do you find Machine Learning Intern roles from employers already enrolled in E-Verify?
Migrate Mate lets you search machine learning intern positions filtered by employers with verified STEM OPT hiring history. You can also cross-reference a company's E-Verify status directly through the E-Verify employer search before applying, which saves time if you're targeting smaller research teams or early-stage companies.
What happens to your work authorization if your OPT expires during H-1B cap season?
If your employer files an H-1B petition on your behalf before your OPT expires, cap-gap protection extends your work authorization through September 30 of that fiscal year. You can continue working in your Machine Learning Intern or full-time ML role during this period without interruption, provided the petition remains pending or approved.
Can a part-time Machine Learning Internship count toward STEM OPT?
USCIS requires STEM OPT employment to be at least 20 hours per week with a qualifying E-Verify employer. A part-time ML internship at or above that threshold is generally acceptable, but the I-983 training plan must reflect a genuine learning experience tied to your STEM degree, not a minimal or administrative role.