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Machine Learning Intern Jobs in Randolph, MA (NOW HIRING)

Meta

Research Scientist Intern, AI Alignment

Boston, MA

$7.65K - $12.13K/mo

Research Scientist Intern, AI Alignment Responsibilities: * Develop novel state-of-the-art ... D. degree in Computer Science, Machine Learning, Artificial Intelligence, or relevant technical ...

Microsoft

Research Intern - Self-Improving AI

Cambridge, MA · On-site

$6.71K - $13.27K/mo

The Machine Learning/Artificial Intelligence (ML/AI) group at Microsoft Research NYC is looking for a Research Intern candidate with a background in language modeling and reinforcement learning, for ...

Dothouse Health

Volunteer/ Intern

Dorchester, MA · On-site

$15.75 - $21/hr

Job Type Part-time Description SUMMARY The Community Health Intern provides an opportunity in a structured learning environment to set and evaluate personal and professional goals and objectives ...

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Machine Learning Intern information

See Randolph, MA salary details

$26K

$43.4K

$89.7K

How much do machine learning intern jobs pay per year?

As of May 30, 2026, the average yearly pay for machine learning intern in Randolph, MA is $43,421.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,100.00 and $46,900.00 per year, depending on experience, location, and employer.

What Does a Machine Learning Intern Do?

A machine learning intern works in the field of data science. During an internship, you work alongside machine learning engineers who are developing artificial intelligence programs. They do this by writing computer code that allows a software system to run autonomously. Your exact responsibilities depend on the type and level of engineering that the company does. While you likely do not have coding duties, you may help the programmers test or debug their code. You may also work with algorithms and the mathematical aspects of artificial intelligence. A machine learning intern works under the supervision of a lead engineer.

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

To thrive as a Machine Learning Intern, you need a solid understanding of statistics, programming (especially Python), and foundational machine learning concepts, typically supported by coursework or a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and data analysis libraries, as well as experience with version control systems like Git, is highly valuable. Strong problem-solving skills, curiosity, and effective communication set outstanding candidates apart in this role. These abilities are essential for analyzing data, building models, and collaborating with teams to develop innovative AI solutions.

What types of projects do Machine Learning Interns typically work on, and how are they supported by the team?

Machine Learning Interns often contribute to real-world projects such as data preprocessing, developing and testing models, or assisting with research for new algorithms. Interns are usually paired with a mentor or work within a small team, receiving guidance during code reviews and regular check-ins. This collaborative environment helps interns gain practical experience, quickly overcome challenges, and integrate feedback, ensuring a steep learning curve and valuable industry exposure.

What is the difference between Machine Learning Intern vs Data Science Intern?

AspectMachine Learning InternData Science Intern
Required CredentialsTypically pursuing or recent graduate in Computer Science, Data Science, or related fields; knowledge of programming and ML frameworksUsually pursuing or recent graduate in Data Science, Statistics, or related fields; strong analytical and programming skills
Work EnvironmentTech companies, research labs, startups focusing on AI/ML projectsBusiness, finance, healthcare, and tech sectors analyzing data for insights
Employer & Industry UsageUsed in companies developing AI products, research institutions, tech startupsCommon in organizations requiring data analysis, reporting, and decision-making support

While both roles involve working with data and programming, a Machine Learning Intern focuses specifically on developing and implementing machine learning models, whereas a Data Science Intern works more broadly on analyzing data, creating reports, and deriving insights. The roles often overlap, but the Machine Learning Intern role emphasizes algorithm development and model deployment.

More about Machine Learning Intern jobs
What job categories do people searching Machine Learning Intern jobs in Randolph, MA look for? The top searched job categories for Machine Learning Intern jobs in Randolph, MA are:
What cities near Randolph, MA are hiring for Machine Learning Intern jobs? Cities near Randolph, MA with the most Machine Learning Intern job openings:
Machine Learning Intern jobs near you

Graduate Intern - Deep Learning in Protein Design

Manus

Waltham, MA

Full-time, Internship

Posted 23 days ago

Job description

Graduate Intern for Deep Learning in Protein Design Boston, Massachusetts, United States, Full-Time

Position Summary

Manus works across industries and value chains to accelerate the transition to BioAlternatives – better performing and more sustainable versions of complex molecules traditionally sourced from plants, animals, or fossil fuels. Our platform is proven to work across scales, bridging the Valley of Death between lab and manufacturing more efficiently and more reliably to deliver the benefits of synthetic biology today.

We are seeking a Graduate Intern in Deep Learning for Protein Design to investigate key gaps in AI-guided protein engineering and explore novel approaches to address them. The ideal candidate has experience applying deep learning methods to biological problems, such as protein sequence design, structure prediction, or mutation effect modeling. This internship offers the opportunity to work at the intersection of machine learning and synthetic biology, contributing directly to scalable, real-world applications.

Why work at Manus:

  • Opportunity – For motivated, results-oriented team members, our growth creates opportunities for personal and professional advancement.
  • Accountability – You are given the resources you need to succeed and the freedom to make it happen; in return, we hold each other accountable for our high expectations.
  • Passion – We love what we do and enjoy working with others who feel the same way. We embrace the challenge and hard work that comes with working on the cutting edge.

Key Responsibilities:

  • Evaluating state-of-the-art deep learning models for mutation effect prediction and identify their limitations (eg. Epistasis, protein dynamics, etc)
  • Drive exploratory research into potential avenues to fill gaps in DL-based approaches
  • Communicate results and insights to multidisciplinary teams, including presentations and written reports

Required Qualifications

  • Currently enrolled in a Masters or PhD program in Computer Science or BioEngineering or similar programs with emphasis on AI applications in Biological systems
  • Demonstrated experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and libraries.
  • Proficiency in programming languages such as Python and familiarity with AI-assisted coding tools (eg., Claude Code, Codex)
  • Excellent verbal and written communication skills

Preferred Qualifications

  • Familiarity with protein engineering (eg. Directed evolution, ML/AI-led)
  • Familiarity with protein language models and similar transformer-based models
  • Familiarity with benchmarking techniques and public datasets for protein sequence to function relationship
  • Experience in industrial biotechnology or a related industry

Preferred Working Style

  • Must be very well-organized and be able to handle multiple projects simultaneously.
  • Must be a quick learner who is self-motivated and able to ask questions and seek clarity.
  • Must be flexible with day-to-day duties and able to thrive in a start-up environment.
  • Must be an excellent team member with strong communication skills and a desire to work collaboratively.
  • Must hold themselves to the highest professional, scientific and ethical standards.