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# distributed compute engineer

- **Company:** Hudson River Trading
- **Location:** United States
- **Salary:** $200,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Unix, C++ (Programming Language), Computer Engineering, Software Debugging, Linux, Python (Programming Language), Network Service, Open Source Technology, Software Systems, Computer Network Technologies, Information Technology, Data Caching
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/software-engineer-distributed-compute-hudson-river-trading-7808816

## About the Role

* 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

## Description

We use a large, customized research environment with highly advanced CPU and GPU compute, storage, and networking technology. Our team thinks about the entire software stack, from the user-level API to optimizing performance out of low-level caching. We deal with everything from the theoretical, like fair resource scheduling, to the concrete, like optimizing a trader's new program run-time performance.

As a distributed compute engineer in a user-facing environment, you'll develop and improve on technical solutions that make research as easy and efficient as possible. You'll work everyday with both talented quantitative traders and software engineers across all trading teams.

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
 * Share metrics and real-time information with users to give insight into workload behavior
 * 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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