Algorithm Development (Quant Research & Trading) PhD Internship – Summer 2027 (Python/C++)

Location: London, United Kingdom; New York, NY, United States; Singapore

Salary: New York: 5,800 USD per week; Singapore: 7,650 SGD per week; London: 4,350 GBP per week. A competitive signing bonus, company-paid housing, meals, and other perks are also offered.

Company

hirify.global applies scientific research and sophisticated computing infrastructure to algorithmic trading across more than 200 markets.

What you will do

• Research and implement automated trading strategies across different time horizons.
• Apply academic research to real-world trading problems and machine learning strategies.
• Use Python/C++ infrastructure and third-party tools for quantitative research and data analysis.
• Apply machine learning and time series techniques to large, complex market datasets.
• Build predictive models for financial markets using market and non-market data.
• Run simulations and process data on a proprietary compute cluster while collaborating across teams.

Requirements

• Full-time PhD student in a quantitative discipline such as mathematics, physics, computer science, statistics, operations research, or machine learning.
• Fluency in Python is required.
• Experience with statistical analysis, numerical programming, or machine learning using Python, Pandas/Numpy, R, and/or MATLAB.
• Strong communication skills.
• Interest in applying research expertise to opportunities in worldwide markets.

Culture & Benefits

• Rotation across teams with collaboration alongside researchers and technologists.
• Tech Talks covering markets and HRT’s trading philosophy.
• Speaker curriculum, trading games, mentorship, and social events throughout the summer.
• Competitive signing bonus, company-paid housing, meals, and additional perks.
• Culture focused on openness, transparency, diverse expertise, and collaboration.

Hiring process

• AI tools are prohibited during interviews or assessments unless explicitly permitted.
• Candidate responses may be evaluated for authenticity.