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Remote Co Op Computer Science Jobs in Boston, MA

Tamr

Marketing Co-Op 2026 (H2)

Cambridge, MA · Remote

$30 - $35/hr

Access to a computer with an internet connection is required. Applicants must be currently pursuing ... Applicants should be available for co-op from August 2026 to December 2026. This position is hybrid ...

MedSchoolCoach

Research Mentor (Remote)

Boston, MA · Remote

$75 - $100/hr

Medical/Biological Sciences Physics, Chemistry Computer Science, AI Environmental Sciences Compensation & Work Structure: $75-$100 per mentorship session Fully Remote (Must have stable internet ...

Ocean Spray

Environmental Engineer

Boston, MA · On-site +1

Whatever your title, whatever your role - it always comes back to this: we're a farmer-owned co-op ... We are open to remote candidates. In this role, the Environmental Engineer is responsible for ...

Pentair

Senior SAP Analyst - CO Expertise

Boston, MA · On-site +1

$84K - $157K/yr

A. or B.S.) in Information Systems, Business, Supply Chain, Computer Science, or a related field ... Remote Equal Opportunity Employer Pentair is an Equal Opportunity Employer. With our expanding ...

Globalchannelmanagement

Remote AI Architect

Boston, MA · Remote

$90 - $92/hr

Remote AI Architect needs 10+ years' experience enterprise-wide AI programs or platform buildouts ... Bachelor's degree in Computer Science, Engineering, or a related technical field. * 5+ years of ...

MedSchoolCoach

Research Mentor

Boston, MA · Remote

$75 - $100/hr

Medical/Biological Sciences Physics, Chemistry Computer Science, AI Environmental Sciences Compensation & Work Structure: $75-$100 per mentorship session Fully Remote (Must have stable internet ...

MedSchoolCoach

Research Mentor

Boston, MA · On-site +1

$75 - $100/hr

Medical/Biological Sciences ⚛ Physics, Chemistry Computer Science, AI Environmental Sciences Compensation & Work Structure: $75-$100 per mentorship session Fully Remote (Must have stable internet ...

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Remote Co Op Computer Science information

How do Remote Co-Op Computer Science roles typically facilitate mentorship and team collaboration despite the virtual setting?

Remote Co-Op Computer Science positions often leverage various collaboration tools such as Slack, Microsoft Teams, and Zoom to foster communication and mentorship. You'll participate in regular virtual stand-ups, code reviews, and project meetings, ensuring you stay connected with your team and receive guidance from senior engineers. Many companies assign a dedicated mentor or buddy to help you navigate both technical challenges and company culture. This structure allows you to build relationships, gain feedback, and make meaningful contributions without being onsite.

What is a Remote Co-Op in Computer Science?

A Remote Co-Op in Computer Science is a paid or unpaid work placement that allows students to gain real-world experience in their field while working from a location outside the traditional office, such as their home. These positions are typically part of a college or university's cooperative education program, blending academic learning with practical work experience. Students work on software development, data analysis, or IT projects under the supervision of professionals, helping them build technical skills and professional networks. Remote Co-Ops offer flexibility and can connect students with companies outside their local area, broadening their career opportunities.

What are the key skills and qualifications needed to thrive as a Remote Co-Op Computer Science student, and why are they important?

To thrive as a Remote Co-Op Computer Science student, you need a solid grasp of programming fundamentals, data structures, algorithms, and typically be pursuing or have completed coursework toward a computer science degree. Familiarity with version control systems like Git, collaborative platforms such as GitHub or Jira, and exposure to coding languages like Python, Java, or C++ are commonly expected. Strong communication, self-motivation, and time management are essential soft skills for remote teamwork and independent learning. These skills ensure you can effectively contribute to projects, adapt to remote workflows, and maximize your experiential learning.

What is the difference between Remote Co Op Computer Science vs Remote Software Intern?

AspectRemote Co Op Computer ScienceRemote Software Intern
CredentialsTypically enrolled in a computer science program, may require coursework or enrollment verificationUsually students pursuing a degree in computer science or related field, may need proof of enrollment
Work EnvironmentRemote, collaborative team settings, often part-time during academic termsRemote, project-based tasks, often part-time or summer internships
Employer & Industry UsageUsed by tech companies, startups, and corporations for student talent pipelinesCommonly offered by tech firms, startups, and software companies for skill development

Both roles are designed for students gaining practical experience in computer science. The main difference lies in the stage of education and the program structure: Co Op positions are typically part of a formal cooperative education program, while Software Internships are often summer or short-term roles. Both provide valuable industry exposure and skill development in remote settings.

What are popular job titles related to Remote Co Op Computer Science jobs in Boston, MA? For Remote Co Op Computer Science jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Remote Co Op Computer Science jobs in Boston, MA look for? The top searched job categories for Remote Co Op Computer Science jobs in Boston, MA are:
Remote Co Op Computer Science jobs near you
Infographic showing various Remote Co Op Computer Science job openings in Boston, MA as of July 2026, with employment types broken down into 66% Full Time, 17% Temporary, and 17% Contract. Highlights an 100% Remote job distribution.

Co-op, Machine Learning for Digital Twins

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted 25 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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