Quantitative Technologist (Full-Time - DevOps & Systems Engineering)

Radix Trading University Job Board•Chicago, IL

13h

About The Position

As a DevOps Engineer, your goal is to enhance live trading systems, research infrastructure, and alpha generation through technology innovation. Our DevOps team is specifically responsible for ensuring the efficacy of our trading machines and the translation of research to production. The ability to execute in a fast-paced environment is crucial for this team, as you’ll be manipulating Linux on the fly, identifying errors and inefficiencies quickly, and designing solutions to challenges with systems, networking, and general architecture on a daily basis. We leverage cutting-edge software and hardware to push production-level code daily and evolve fully-automated strategies, alpha generation processes, live trading systems, and machine learning research and simulation infrastructure used to analyze petabytes of data. We provide starter projects that we know will add value to the firm, while encouraging exploration and independent projects to improve inefficiencies, propose iterations, and own significant processes and tools within your first few months.

Requirements

Python; awareness of strength in particular language and ability to solve more complex problems due to understanding nuances of the language
Persistent Drive to Improve
Creative Problem Solving and Probabilistic Thinking
Team Mindset
Mental Flexibility & Self Awareness
Orientation for Making Money
Software development skills to have or develop
Understanding of data structures, algorithmic complexities, and numerical algorithms
Systems Architecture - knowledge and experience with distributed systems, communication, and HPC systems
Software Architecture - knowledge and experience working with a large code base
Development Processes - experience delivering and deploying software in production environments

Responsibilities

Enhance live trading systems, research infrastructure, and alpha generation through technology innovation.
Ensure efficacy of trading machines and the translation of research to production.
Manipulate Linux on the fly.
Identify errors and inefficiencies quickly.
Design solutions to challenges with systems, networking, and general architecture.
Push production-level code daily.
Evolve fully-automated strategies, alpha generation processes, live trading systems, and machine learning research and simulation infrastructure.
Improve inefficiencies, propose iterations, and own significant processes and tools.