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Internship Risk Engineer Jobs in Boston, MA (NOW HIRING)

Analogdevices

Engineer, Quality Engineering

Wilmington, MA · On-site

$78K - $101K/yr

Perform risk assessments and support the development of risk management plans. * Review and approve ... Internship or co-op experience in a quality engineering role within the medical device industry ...

Analog Devices

Engineer, Quality Engineering

Wilmington, MA · On-site

$78K - $101K/yr

Perform risk assessments and support the development of risk management plans. * Review and approve ... Internship or co-op experience in a quality engineering role within the medical device industry ...

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All JobsRisk Engineer JobsInternship Risk Engineer JobsInternship Risk Engineer Jobs in Boston, MA

Internship Risk Engineer information

See Boston, MA salary details

$14

$27

$42

How much do internship risk engineer jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for internship risk engineer in Boston, MA is $27.61, according to ZipRecruiter salary data. Most workers in this role earn between $22.45 and $31.35 per hour, depending on experience, location, and employer.

What is the difference between Internship Risk Engineer vs Risk Engineer?

AspectInternship Risk EngineerRisk Engineer
Required CredentialsTypically pursuing or recently completed a relevant degree; internships may not require certificationsProfessional certifications like ASP, ARM, or PE often preferred
Work EnvironmentEntry-level, supervised, and learning-focused roles within companies or consulting firmsFull-time, experienced roles involving risk assessment, analysis, and mitigation strategies
Employer & Industry UsageUsed by companies during internship programs to train future risk professionalsUsed by industries such as insurance, construction, manufacturing, and energy for ongoing risk management

The main difference is that an Internship Risk Engineer is an entry-level, learning position often held by students or recent graduates, while a Risk Engineer is a full-time professional responsible for assessing and managing risks in various industries. Internships serve as a stepping stone toward becoming a full Risk Engineer with more responsibilities and certifications.

What are the most commonly searched types of Risk Engineer jobs in Boston, MA? The most popular types of Risk Engineer jobs in Boston, MA are:
What cities near Boston, MA are hiring for Internship Risk Engineer jobs? Cities near Boston, MA with the most Internship Risk Engineer job openings:
Internship Risk Engineer jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 4 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)