Hudson River Trading - Software Engineer
Requirements
• Bachelor's degree in Computer Science, Computer Engineering, or a related field • Strong C++ expertise (daily use preferred) • Python familiarity preferred • Experience collaborating closely with quantitative researchers and traders to understand their needs • Great communication capabilities • Superior design, debugging, and problem solving skills • Knowledge of UNIX operating systems (we use Linux), system/processor performance, and network communication • The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package. • Hudson River Trading (HRT) brings a scientific approach to trading financial products. We have built one of the world's most sophisticated computing environments for research and development. Our researchers are at the forefront of innovation in the world of algorithmic trading. • At HRT we welcome a variety of expertise: mathematics and computer science, physics and engineering, media and tech. We’re a community of self-starters who are motivated by the excitement of being at the cutting edge of automation in every part of our organization—from trading, to business operations, to recruiting and beyond. We value openness and transparency, and celebrate great ideas from HRT veterans and new hires alike. At HRT we’re friends and colleagues – whether we are sharing a meal, playing the latest board game, or writing elegant code. We embrace a culture of togetherness that extends far beyond the walls of our office.
Responsibilities
• Ensure HRT’s research environment is best in class, with a focus on user workloads • Maintain and improve resource scheduling, data caching, and job monitoring to make research as easy, fast, and efficient as possible, using both in-house and open source solutions • Optimize workloads at the user level to speed up user iteration speed • Tune workloads at the infrastructure level to make them more resource-efficient • Sit down with users to understand their specific technical needs • Contribute individually and through leadership and coordination of the above • Set technical direction for the platform and take holistic ownership of the software infrastructure that supports Algo research
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