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

Microsoft

Research Intern - Self-Improving AI

Cambridge, MA · On-site

$6.7K - $13K/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 ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... Foundational understanding of machine learning concepts - supervised learning, model evaluation ...

DataRobot, Inc.

Agentic AI Intern

Boston, MA · On-site

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... Foundational understanding of machine learning concepts - supervised learning, model evaluation ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... Foundational understanding of machine learning concepts - supervised learning, model evaluation ...

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Showing results 21-40

All JobsMachine Learning Intern JobsMachine Learning Intern Jobs in Allston, MA

Machine Learning Intern information

See Allston, MA salary details

$27.8K

$46.5K

$96K

How much do machine learning intern jobs pay per year?

As of Aug 20, 2026, the average yearly pay for machine learning intern in Allston, MA is $46,466.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,500.00 and $50,200.00 per year, depending on experience, location, and employer.

What does a machine learning intern do?

A Machine Learning Intern assists with developing, testing, and deploying machine learning models under the supervision of experienced data scientists or engineers. Their responsibilities may include data preprocessing, feature engineering, coding algorithms, analyzing results, and assisting with research tasks. Interns often work with programming languages like Python and libraries such as TensorFlow or PyTorch. The internship provides hands-on experience in real-world machine learning projects and helps interns build essential skills for a future career in the field.

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 Allston, MA look for?

The top searched job categories for Machine Learning Intern jobs in Allston, MA are:

What cities near Allston, MA are hiring for Machine Learning Intern jobs?

Cities near Allston, MA with the most Machine Learning Intern job openings:

Machine Learning Intern jobs near you
Infographic showing various Machine Learning Intern job openings in Allston, MA as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $46,466 per year, or $22.3 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 14 days ago

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)