Principal Software Engineer

Fidelity Investments•Jersey City, NJ

2d•$107,000 - $216,000•Onsite

About The Position

The Team Fidelity Wealth’s Trade Management Engineering group is responsible for building world-class electronic trading solutions for Fidelity’s Capital Markets division. We are a high-performing, fast-paced technology team working with cutting-edge tools to deliver low-latency, high-throughput trading platforms. Our work spans across internal and external teams to build seamless, end-to-end electronic trading workflows. The Role We are seeking a highly experienced software engineer to lead the design and development of next-generation trading systems. This is a hands-on technical leadership role focused on building scalable, resilient, and high-performance trading infrastructure. You’ll collaborate across teams, mentor engineers, and drive innovation in a mission-critical environment.

Requirements

Bachelor’s degree in Mathematics, Computer Science, Engineering, Information Technology, or equivalent.
10 years professional experience in quantitative finance or trading systems
Advanced proficiency in KDB/q, including: Time‑series data modeling, High‑performance querying and joins, Real‑time and historical analytics
Strong Python skills for: Quantitative analysis, AI / ML model development, Integration with KDB and downstream systems
Experience working with large-scale, high‑frequency, or noisy datasets
Solid software engineering practices (Git, testing, modular design)
Worked with AI developer assist tools (e.g. GitHub Copilot).
Experience with CI/CD tools such as GitHub, Maven, Jenkins, Artifactory, and uDeploy.
Hands-on experience in AWS or other cloud platforms.
Familiarity with object-oriented programming languages such as Java
Experience in Linux, shell scripting, and production support experience

Nice To Haves

A strong quantitative mindset with practical AI application skills
Ability to bridge research, machine learning, and production systems
Comfort working on front‑office or research-critical infrastructure
Clear communicator with quants, traders, and engineers
Willingness to support production systems and participate in on-call rotations, including occasional weekend support.

Responsibilities

Design, develop, and optimize KDB databases and q analytics for high‑volume trading and market data
Develop Python-based AI and quantitative models for research, prediction, classification, and signal generation
Apply machine learning techniques to time‑series data (feature engineering, model training, evaluation)
Build research and backtesting frameworks integrating AI models with historical data
Translate quantitative and ML research into robust, production-ready systems
Integrate AI models into real-time and batch pipelines
Optimize analytics and model evaluation for performance, stability, and scalability
Collaborate with quants, product owners, and engineering teams on model deployment and monitoring