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Bioinformatics Machine Learning Internship Jobs in Cleveland, OH

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Bioinformatics Machine Learning Internship information

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$24.7K

$41.3K

$85.3K

How much do bioinformatics machine learning internship jobs pay per year?

As of Sep 8, 2026, the average yearly pay for bioinformatics machine learning internship in Cleveland, OH is $41,299.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,500.00 and $44,600.00 per year, depending on experience, location, and employer.

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 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 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 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 Cleveland, OH?

The most popular types of Bioinformatics Machine Learning jobs in Cleveland, OH are:

What are popular job titles related to Bioinformatics Machine Learning Internship jobs in Cleveland, OH?

For Bioinformatics Machine Learning Internship jobs in Cleveland, OH, the most frequently searched job titles are:

What job categories do people searching Bioinformatics Machine Learning Internship jobs in Cleveland, OH look for?

The top searched job categories for Bioinformatics Machine Learning Internship jobs in Cleveland, OH are:

What cities near Cleveland, OH are hiring for Bioinformatics Machine Learning Internship jobs?

Cities near Cleveland, OH with the most Bioinformatics Machine Learning Internship job openings:

Bioinformatics Machine Learning Internship jobs near you
Infographic showing various Bioinformatics Machine Learning Internship job openings in Cleveland, OH as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $41,299 per year, or $19.9 per hour.

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Block

Cleveland, OH • On-site

Other

Posted 9 days ago

Key responsibilities

  • Own a research problem end-to-end by framing questions, developing methods, running experiments, and publishing findings.

  • Build systems that understand customer goals, context, and behavior to enable proactive intelligence and decision-making.

  • Develop evaluation frameworks to measure system performance, trust, and customer value in real-world scenarios.

Block rating

7.9

Company rating: 7.9 out of 10

Based on 16 frontline employees who took The Breakroom Quiz

9th of 21 rated payment service providers

Job description

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op)

About Apollo

Apollo leads Block's efforts to build the Customer World Model (CWM): a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs.

The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf.

We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer.

About the role

We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence.

This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers.

You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time.

Past interns have shipped production systems within months and published their work in the same year.

What you'll work on

Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas:

Customer World Models

Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data.

Examples include:

  • Representation learning over long-horizon customer histories
  • Event-based foundation models
  • Multi-modal customer representations spanning structured, sequential, and graph data
  • Memory architectures for long-term customer understanding

Proactive Intelligence

Developing systems that can anticipate customer needs and initiate helpful actions before being asked.

Examples include:

  • Opportunity detection and next-best-action systems
  • Long-horizon planning and decision-making
  • Preference and goal inference
  • Learning when intervention creates value versus friction

Agentic Decision Systems

Building agents that reason over customer world models and take actions in real environments.

Examples include:

  • Tool use and planning
  • Multi-step reasoning over customer state
  • Autonomous workflow execution
  • Recovery and adaptation under uncertainty

Learning from Feedback Loops

Developing methods that allow intelligence to improve continuously from real-world outcomes.

Examples include:

  • Reinforcement learning from customer and product feedback
  • Reward modeling and preference learning
  • Counterfactual evaluation
  • Credit assignment over long decision horizons

Evaluation and Measurement

Building evaluation frameworks that predict real-world performance, trust, and customer value.

Examples include:

  • Simulated customer environments
  • Longitudinal evaluation
  • Decision quality metrics
  • Safety and reliability benchmarks

What we're looking for

We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time.

Required

  • Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op.
  • Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models.
  • Experience conducting independent research and translating ideas into working systems.
  • Fluency in Python and experience with PyTorch, JAX, or similar frameworks.
  • Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work.

Nice to have

  • Experience with large language models and agentic systems.
  • Experience with reinforcement learning, reward modeling, or sequential decision-making.
  • Experience with representation learning for structured, temporal, or graph data.
  • Familiarity with large-scale training and production ML systems.
  • Interest in building AI systems that directly affect customer outcomes.

What you'll get

  • Direct mentorship from researchers working on the future of proactive intelligence at Block.
  • Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources.
  • Opportunities to publish and contribute to open-source projects.
  • A chance to shape foundational technology that could power the next generation of Block products.
  • Exposure to both scientific research and product deployment, with a clear path from idea to impact.

Application Guidelines

Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.

Use of AI in Our Hiring Process

We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.

Contact us here with hiring practice or data usage questions.

Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block.

Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

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