Quantitative Developer Intern — Build Automated Trading

Walleye Capital Full Time; Boston (MA); On-site; USD 203,000 - 243,000; Full time

Benefits offered by this job

Housing stipend

Domestic travel to Boston

Job summary

Walleye Capital seeks a Quantitative Developer Intern for the Quantic team in Boston. You will develop quantitative infrastructure, work with traders and researchers on proprietary strategies, and deploy analytics for alpha generation and risk assessment.

Ideal candidates are pursuing CS/engineering/math degrees, with Python, ML and Unix/Linux experience, and an interest in financial markets and AI applications within a fast-paced trading environment.

Qualifications

• Pursuing an undergraduate or advanced degree in relevant field with graduation between Dec 2027 and Jun 2028.
• Proficient in a scripting language (Python/Bash/Perl) and UNIX/Linux environments.
• Familiar with ML/DL/statistical packages (TensorFlow, PyTorch, scikit-learn).
• Self-starter who can work independently and in a team.
• Interest in financial markets, systematic investing, and AI/LLM applications.

Responsibilities

• Develop and deploy quantitative infrastructure for alpha generation, portfolio construction, and algorithmic trading.
• Design and manage data pipelines; improve data integrity and traceability of financial datasets.
• Collaborate with traders and researchers to develop and iterate on proprietary strategies and alphas.
• Build reporting tools for strategy risk, trade costs, and execution using proprietary data.
• Apply coding and AI tools to oversee and improve automated trading systems.

Skills

Python

UNIX/Linux

Statistics

Mathematics

Machine Learning

Bash

Education

CS/Engineering/Statistics/Math degree

Tools

TensorFlow

PyTorch

scikit-learn

Unix/Linux environments

Job description

Walleye Capital seeks a Quantitative Developer Intern for the Quantic team in Boston. You will develop quantitative infrastructure, work with traders and researchers on proprietary strategies, and deploy analytics for alpha generation and risk assessment.

Ideal candidates are pursuing CS/engineering/math degrees, with Python, ML and Unix/Linux experience, and an interest in financial markets and AI applications within a fast-paced trading environment.