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Machine Learning Co Op Jobs in Boston, MA (NOW HIRING)

NorthEastern

Assistant/Associate Co-op Coordinator

Boston, MA · On-site

$19.75 - $27.25/hr

Integrated Learning : Cooperative education is an integral part of the academic experience at ... The Co-op Coordinator shares knowledge of industry with academic faculty to integrate employment ...

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All JobsMachine Learning Co Op JobsMachine Learning Co Op Jobs in Boston, MA

Machine Learning Co Op information

See Boston, MA salary details

$27.7K

$46.3K

$95.6K

How much do machine learning co op jobs pay per year?

As of Jul 4, 2026, the average yearly pay for machine learning co op in Boston, MA is $46,263.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,300.00 and $50,000.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Co Op vs Data Scientist?

AspectMachine Learning Co OpData Scientist
Required CredentialsTypically pursuing a degree in CS, Data Science, or related fields; internships often preferredUsually holds a bachelor's or master's in Data Science, Statistics, or related fields; advanced certifications beneficial
Work EnvironmentInternship setting, often part-time or seasonal, in tech or research companiesFull-time role in various industries, including tech, finance, healthcare, with collaborative teams
Employer & Industry UsageUsed by companies for training and evaluating potential future employees; common in tech and research sectorsHired for analyzing data, building models, and deriving insights; prevalent across multiple industries

While both roles involve working with data and algorithms, a Machine Learning Co Op is typically an internship aimed at gaining experience, whereas a Data Scientist is a full-time professional responsible for developing and deploying data models. The Co Op provides a stepping stone into the field, often leading to a full-time Data Scientist position.

What types of projects do Machine Learning Co-Op students typically work on, and how do they contribute to the team?

Machine Learning Co-Op students often work on a variety of hands-on projects, such as developing data preprocessing pipelines, training and evaluating machine learning models, or supporting ongoing research initiatives. They commonly collaborate with data scientists, engineers, and other interns, contributing fresh perspectives and technical support. Co-Ops may also participate in code reviews, attend team meetings, and present their findings, making them valuable contributors to both experimental and production-level work. This collaborative environment offers plenty of opportunities to learn from experienced professionals while making a real impact on projects.

What is a Machine Learning Co-Op?

A Machine Learning Co-Op is a temporary, paid position that allows students or recent graduates to gain hands-on experience working with machine learning technologies in a professional setting. Co-ops typically last several months and are designed to provide practical exposure to real-world projects, such as building models, analyzing data, and collaborating with data scientists or engineers. This role helps participants develop technical skills, gain industry insights, and build a professional network, which can be valuable for future career opportunities in the field of artificial intelligence or data science.

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

To thrive as a Machine Learning Co Op, you need strong programming skills (especially in Python), a solid foundation in mathematics and statistics, and coursework or experience in data science or machine learning. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is typically expected. Excellent problem-solving abilities, eagerness to learn, and effective communication help set you apart in collaborative and fast-paced environments. These skills and qualities are crucial for successfully contributing to real-world projects and advancing your expertise in the field.
What are the most commonly searched types of Machine Learning jobs in Boston, MA? The most popular types of Machine Learning jobs in Boston, MA are:
What cities near Boston, MA are hiring for Machine Learning Co Op jobs? Cities near Boston, MA with the most Machine Learning Co Op job openings:
Machine Learning Co Op jobs near you

Co-op, Machine Learning for Digital Twins

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted 22 days ago

Job description

Your Impact at LILA

Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our team partners with the diverse experimental groups to build digital twins of experimental campaigns, focusing on calibrated, uncertainty-aware models that enable higher-throughput, higher-quality use of Lila's AI Science Facilities (AISF).

As an ML for Digital Twins Co-Op, you will work on building, training, and evaluating ML models for physical and experimental systems. You will get hands-on experience with operator learning, surrogate modeling, and uncertainty quantification, shipping work that directly informs how next-generation AISF experiments are designed and run.

What You'll Be Building

  • Contribute to ML models for scientific and experimental systems, focused on a well-defined digital twin sub-problem
  • Build and train surrogate, operator-learning, or physics-informed models against experimental and simulation data, with mentor guidance
  • Calibrate models, quantify uncertainty, and validate against data flowing from active AISF experimental campaigns
  • Frame open-ended scientific questions as concrete ML tasks with clear datasets, baselines, and evaluation criteria
  • Document findings and share results in cross-departmental collaboration through write-ups and presentations

What You'll Need to Succeed

  • Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics, Materials Science, Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related quantitative field (PhD preferred)
  • Strong programming skills in Python and hands-on experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar
  • Experience applying machine learning to scientific, engineering, physical, or experimental systems
  • Familiarity with neural operators, operator learning, spatiotemporal modeling, field prediction, dynamical systems, scientific computing, surrogate modeling, or physics-informed ML
  • Ability to turn open-ended scientific questions into concrete ML tasks with clear datasets, assumptions, baselines, and evaluation criteria
  • Solid foundation in model training, validation, debugging, experiment tracking, and performance evaluation
  • Comfort working with messy, heterogeneous, or evolving scientific datasets
  • Clear communication and interest in collaborating across ML, software engineering, and physical science teams

Bonus Points For

  • Experience with modern operator-learning methods, including Fourier Neural Operators, DeepONets, graph neural operators, transformer-based neural operators, attention-based operators, physics-informed operators, or operator learning for spatiotemporal systems
  • Experience with digital twins, model update, calibration, and uncertainty-aware scientific modeling, including online/offline model updating, simulator calibration, discrepancy modeling, uncertainty quantification, out-of-distribution detection, or reliability estimation
  • Experience with closed-loop scientific decision-making or physical science applications, including active learning, Bayesian optimization, design of experiments, experimental decision-making, or applications in materials science, chemistry, energy systems, catalysis, batteries, electrochemistry, additive manufacturing, fluid dynamics, thermodynamics, robotics, or computational physics

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