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Internship Computer Science Research Jobs in New York

New York University

Research Intern

Manhattan, NY · On-site

$48K - $62K/mo

Description RESEARCH INTERNSHIP Department of Computer Science and Engineering New York University The Department of Computer Science and Engineering at NYU Tandon School of Engineering is seeking to ...

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What is the difference between Internship Computer Science Research vs Software Developer Intern?

AspectInternship Computer Science ResearchSoftware Developer Intern
Required CredentialsRelevant coursework, research experience, possibly a related degreeProgramming skills, coursework in software development, sometimes a degree in CS or related field
Work EnvironmentAcademic or research labs, university projects, research institutionsTech companies, startups, software firms, corporate offices
Employer & Industry UsageUniversities, research labs, government agenciesPrivate tech companies, software firms, startups
Common Search & Comparison IntentUnderstanding research-focused internships in CSExploring software development internship opportunities

Internship Computer Science Research typically involves working on academic or scientific projects, emphasizing research methodologies and theoretical understanding. In contrast, a Software Developer Intern focuses on coding, building applications, and practical software development skills. Both roles require programming knowledge but differ in environment and objectives, catering to different career paths within computer science.

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The most popular types of Computer Science Research jobs in New York are:

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Internship Computer Science Research jobs near you
Infographic showing various Internship Computer Science Research job openings in New York as of August 2026, with employment types broken down into 14% Internship, 58% Full Time, 14% Part Time, and 14% Temporary. Highlights an 72% In-person, 14% Hybrid, and 14% Remote job distribution.

Research Scientist/Research Engineer, Reinforcement Learning

P2P

Manhattan, NY • On-site

$200 - $350/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago

Key responsibilities

  • Design, evaluate, and optimize reinforcement learning policy architectures, reward formulations, and evaluation methods.

  • Partner with trading and research teams to source, integrate, and validate alpha signals within the reinforcement learning framework.

  • Build and maintain systems for storing, processing, and analyzing large volumes of market and signal data.

Job description

Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

Our team is a group of quantitative researchers, engineers, and ML experts leading reinforcement learning research and trading at Jump. Our mission is to combine emerging techniques and original research to learn optimal decision-making policies from financial market data and monetize them globally. We are building the future of ML-powered trading through breakthrough reinforcement learning, and we're looking for an exceptional Research Scientist/Research Engineer to join our team.

What You’ll Do

As a Research Scientist/Research Engineer working on RL, you'll be at the forefront of applying reinforcement learning to markets. You'll conduct original research and own the systems that turn it into production trading: designing and evaluating policy architectures, reward formulations, and objective horizons with rigorous out-of-sample benchmarking; partnering with trading and research teams to source, integrate, and validate their alpha signals within the RL framework; ensuring simulation fidelity against live trading by modeling market microstructure, fill dynamics, liquidity, and latency; building efficient tooling to store, process, and analyze very large volumes of market and signal data; and communicating findings to technical and trading audiences. This isn't incremental optimization; we're pushing the boundaries of what reinforcement learning can do at scale, where your improvements directly impact live trading.

Other duties as assigned or needed.

Skills You’ll Need
  • 5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia
  • Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production
  • Proficiency in Python and/or C++
  • Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX
  • Strong foundation in mathematics and statistics
  • PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)
  • Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent
  • Ability to thrive in a collaborative, team-oriented environment
  • Creative thinkers who are driven, self-motivated, and eager to solve challenging problems
  • Reliable and predictable availabilityExcellent written and verbal communication skills in English
Benefits
  • Discretionary bonus eligibility
  • Medical, dental, and vision insurance
  • HSA, FSA, and Dependent Care options
  • Employer Paid Group Term Life and AD&D Insurance
  • Voluntary Life & AD&D insurance
  • Paid vacation plus paid holidays
  • Retirement plan with employer match
  • Paid parental leave
  • Wellness Programs
Annual Base Salary Range

$200,000—$350,000 USD

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