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Internship Competitive Programming Jobs (NOW HIRING)

Avista Realtime Systems LLC

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SCADA & Controls Technician Intern

Elkridge, MD · On-site

$32 - $35/hr

Electrical Engineering Technology * Computer Engineering Technology * Industrial Automation ... Paid internship * Competitive hourly compensation * Overtime opportunities when applicable

Avista Realtime Systems LLC

Be Seen First

SCADA & Controls Technician Intern

Elkridge, MD · On-site

$32 - $35/hr

Electrical Engineering Technology * Computer Engineering Technology * Industrial Automation ... Paid internship * Competitive hourly compensation * Overtime opportunities when applicable

Vets2PM

DoD SkillBridge: Maintenance Tech

Holland, MI · On-site

Our 100% vertical integration capabilities allow us to offer our customers end-to-end engineering ... Why you'll enjoy working here (if you are hired after the internship): Competitive Wages and ...

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Internship Competitive Programming information

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How much do internship competitive programming jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for internship competitive programming in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What types of projects or challenges can I expect to work on during a Competitive Programming internship?

During a Competitive Programming internship, you can expect to tackle algorithmic challenges, optimize solutions for efficiency, and participate in problem-solving sessions, often as part of a team. Interns frequently work on creating or testing problems for contests, developing tools or scripts to support competition platforms, and collaborating closely with experienced programmers to review and debug code. These experiences not only strengthen your coding skills but also familiarize you with real-world applications of algorithms and team-based development workflows. Exposure to peer code reviews and regular feedback sessions is also common, helping you grow both technically and professionally.

What is the difference between Internship Competitive Programming vs Software Developer Intern?

AspectInternship Competitive ProgrammingSoftware Developer Intern
Required SkillsAlgorithm design, problem-solving, coding proficiencyProgramming, software development, coding skills
Work EnvironmentCompetitive programming contests, online platforms, hackathonsSoftware development teams, tech companies, project-based work
Industry UsageUsed for skill demonstration, coding competitions, prep for tech rolesUsed for gaining industry experience, software project work

Internship Competitive Programming focuses on honing algorithmic skills through contests and online platforms, often used for skill validation and preparation for technical roles. Software Developer Internships involve working on real-world projects within a company's development team, emphasizing practical software engineering experience. Both roles require strong coding skills but differ in environment and purpose.

What is an internship in competitive programming?

An internship in competitive programming is a structured work experience for students or recent graduates that focuses on developing algorithmic problem-solving skills, programming proficiency, and familiarity with coding competitions. Interns typically work on challenging coding problems, participate in contests, and may help design or test problems for competitions. Such internships are often offered by tech companies, educational organizations, or competitive programming platforms, and can provide valuable experience for careers in software engineering or related fields.

What are the key skills and qualifications needed to thrive as an Internship Competitive Programming, and why are they important?

To thrive as an Internship Competitive Programming, you need a solid grasp of algorithms, data structures, and strong problem-solving abilities, often demonstrated through participation in programming contests and relevant coursework. Familiarity with coding platforms like Codeforces, LeetCode, or HackerRank, and proficiency in languages such as C++, Java, or Python, are typically expected. Analytical thinking, perseverance, and teamwork are valuable soft skills that distinguish top candidates in this role. These skills are crucial for quickly solving complex problems, collaborating effectively, and excelling in high-pressure, time-sensitive environments.
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What are the most commonly searched types of Competitive Programming jobs? The most popular types of Competitive Programming jobs are:
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Internship Competitive Programming jobs near you
Infographic showing various Internship Competitive Programming job openings in the United States as of June 2026, with employment types broken down into 7% Locum Tenens, 8% As Needed, 69% Full Time, 10% Part Time, 4% Temporary, and 2% Nights. Highlights an 85% Physical, 1% Hybrid, and 14% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

Ph.D. Graduate Intern Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA

Full-time

Posted 2 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 applyespecially 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 Youll 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)