Graham Capital Management, L.P. ("Graham") is an alternative investment manager founded in 1994 by Kenneth G. Tropin. Specializing in discretionary and quantitative macro strategies, Graham is dedicated to delivering strong, uncorrelated returns across a wide range of market environments. As one of the industry’s longest-standing global macro and trend-following managers, Graham remains committed to innovation, evolving its strategies through a robust investment, technology, and operational infrastructure. Graham harnesses the synergies between its discretionary and quantitative trading businesses to offer a broad suite of complementary alpha strategies, each built on the principles of thoughtful portfolio construction, active risk management, and diversification by design. Graham invests significant proprietary capital alongside its clients – including global institutions, endowments, foundations, family offices, sovereign wealth funds, investment management advisors, and qualified individual investors – reinforcing alignment of interests across all strategies.
The foundation of Graham’s sustainability and success is the experience and contributions of its people. The firm seeks to cultivate talent, encourage the diversity of ideas, and respect the contributions of all. In turn, each employee shares in the responsibility of strengthening those around them.
Description
Graham Capital Management, L.P. is seeking an AI/ML Quantitative Research Manager to join our Quantitative Strategies team and lead collaborative AI/ML team research efforts. This individual will report into and work closely with our Chief Investment Officer of Quantitative Strategies to assess existing AI/ML capabilities, define a research agenda to improve AI/ML capabilities in current systematic trading systems, and create new AI/ML trading signals to complement and diversify the firm’s main strategies. The individual will maximize performance and competitiveness by utilizing advanced methods in quantitative analysis, risk management and portfolio optimization.
Responsibilities
- Work directly with the CIO-Quantitative Strategies to shape the long‑term AI/ML research roadmap
- Design, prototype, and develop production‑grade AI/ML models that ingest large‑scale alternative data sets to generate tradable signals to make existing trading signals more efficient, profitable and robust
- Work collaboratively in a research team environment, using common development tools to facilitate robust implementation of AI/ML research, production versions of trading systems, and ensure models are robust, scalable, and compliant
- Interact with other departments – data science, technology, operations, trading, marketing, and accounting - to ensure current and proposed ideas are implemented, monitored and executed efficiently and accurately
- Enhance the scalability and flexibility of portfolios to accommodate bespoke investment needs of clients
- Identify and integrate novel big‑data sources into the firm’s data pipeline
- Produce white‑papers, internal presentations, and external publications on AI/ML advances; deliver presentations at industry conferences
- Regularly present findings and ideas to management and investment committee
- Complete other projects as requested by senior management
Requirements
- PhD or MS in a quantitative field (e.g., Computer Science, Machine Learning, Statistics, or a related AI/ML discipline)
- 7+ years of hands‑on AI/ML research and production deployment in a quantitative or data‑science environment
- Strong understanding of systematic trading concepts; financial experience is strongly desired but secondary to AI technical expertise
- Demonstrated fundamental research experience in AI/ML (e.g., publications, conference presentations)
- Advanced programming experience in languages suited for quantitative research and strategy implementation
- Ability to synthesize complex topics into easily digestible verbal and written commentary
- Exposure to securities and derivatives markets and investment processes with knowledge in mid-to-low frequency systematic strategies (preferred)
- Experience managing a team of quantitative researchers is a plus
This role requires commuting into our Rowayton, CT, New York, NY, or London, UK office Mondays through Fridays. Please note that our central Quantitative Research team sits out of Rowayton, CT.
Base Salary Range
The anticipated base salary range for this position is $250,000 to $300,000. The anticipated range is based on information as of the time this post was generated and does not include any discretionary bonus or benefits (see eligibility below). The applicable annual salary or hourly rate paid to a successful applicant will be determined based on multiple factors, including without limitation the nature and extent of prior experience, skills, and qualifications. This wage range may be modified in the future.
In addition, the employee who fills this role will be eligible for a discretionary annual bonus, as well as a wide array of benefit programs, such as medical and life insurance, 401(K) plans, and access to other healthcare programs.
Notes:
No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, benefits, or any other form of compensation that a particular employee may be eligible for remains in the Company's sole discretion unless and until paid and may be modified at Graham’s sole discretion, consistent with the law.
Graham is committed to providing equal employment opportunity to all employees and applicants for employment without regard to their race, color, religious creed, gender, age, national origin, ancestry, alienage, citizenship status, handicap, disability, marital status, sexual orientation, gender identity, pregnancy, childbirth or other related conditions, military status, genetic information, or any other personal characteristics protected by applicable law. This policy applies to all terms and conditions of employment, including hiring, placement, promotion, layoff, termination, transfer, leave of absence and compensation.
