Systematic Fast Trading Intern
About The Position
Man Systematics’ Fast Trading Strategies (FTS) team develops high-Sharpe, high-frequency trading strategies across asset classes and manages a portfolio across global futures and cash markets. The team develops its fast-alpha portfolio end to end, using machine learning, event-driven and market-microstructure techniques, building a customized monetization stack, and optimizing trading and order placement with dedicated high-frequency infrastructure. This internship is for candidates interested in the New York office, which focuses on high-frequency alpha research, trading, and infrastructure. The team is hiring PhD students with strong academic achievement in highly technical degree programs for an expected 10–12-week summer internship. Summer internships are open to penultimate-year students only. The role may focus on machine learning engineering (MLE) or quantitative research (QR). MLE interns work on software for machine-learning applications, including MLOps and research workflows. QR interns use high-frequency tick data and machine-learning algorithms to predict asset returns and convert models into profitable trading strategies, working closely with MLOps to deploy models. Man Systematics describes its culture as performance-driven, meritocratic, flat, open, transparent, and collaborative, with opportunities to grow and contribute. The team engages with the broader research and academic community and holds annual away days and research off-sites. The anticipated US base salary range is $50–$60 USD; compensation packages also include benefits and a discretionary bonus.
Requirements
- Be a PhD student with strong academic achievement in a highly technical degree program.
- Be a penultimate-year student; summer internships are open to penultimate-year students only.
- Be interested in a summer internship expected to last 10–12 weeks.
- For MLE, have coding experience in a variety of languages, especially Python, and knowledge of machine-learning algorithms and research processes.
- For QR, be a strong programmer in a variety of languages, especially Python.
Nice To Haves
- Previous machine-learning or statistical research internship experience in finance, technology, or academia is strongly preferred for QR candidates.
Responsibilities
- For MLE work, develop and use software focused on machine-learning applications, including MLOps and research workflows.
- For QR work, use high-frequency tick data and machine-learning algorithms to predict asset returns and turn models into profitable trading strategies.
- Work closely with MLOps to deploy models in the QR role.
Benefits
- Discretionary bonus
- Flexible working arrangements
- Two annual paid 'Mankind' days for community volunteering
- Competitive holiday entitlements
- Pension/401k
- Life and long-term disability coverage
- Group sick pay
- Enhanced parental leave
- Long-service leave
- Depending on location, private medical coverage, discounted gym membership options, and pet insurance
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
