Research Technology Support

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Research Technology Support Hong Kong About Cubist: Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. Role: Cubist is looking for a support engineer/quantitative developer who will work on a growing L2/L3 support team for quantitative trading systems, high‑frequency trading infrastructure, real time analytics, and research infrastructure. This role will require close collaboration with multiple teams at Cubist, including multiple Portfolio Manager (PM) teams, Central Research Technology, and Execution Services. The role offers career growth through the opportunity to work on the development of the underlying systems. Role/Responsibilities: - Troubleshooting/resolution of quantitative trading systems and other live analytics processes - System monitoring, including development of better processes and tools - New feature development in Python/C++ - Configuration and change management - Onboarding assistance Requirements: - 2+ years of professional support and/or software engineering experience - Experience with trading systems support (co-located low-latency systems a strong plus) - Fluency with C++, Python and SQL - Familiarity with software delivery lifecycle and production deployment - Cloud experience is a strong plus - Team player and strong communicator

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