Software Engineer Professional, Financial Engineering

Freddie Mac•McLean, VA

1d•$109,000 - $163,000

About The Position

Freddie Mac’s Financial Engineering team is seeking a Software Engineer Professional to develop production systems involving data science and machine learning. The role partners with the Single-Family Collateral Modeling team, which is responsible for the firm’s Automated Valuation Model, and builds systems that make models, data, and analytics available for business use. The systems must be robust, scalable, and fault-tolerant. The engineer implements models and data transformations using Python-based technologies, exposes models through web service APIs, deploys services to AWS EKS, and optimizes code for runtime and memory usage. The role also includes automated testing, peer review, and collaboration with teammates on design and implementation challenges. Freddie Mac describes a comprehensive total rewards package with competitive compensation and market-leading benefit programs. The position is full-time, non-exempt, and eligible to participate in the annual incentive program.

Requirements

At least 2–4 years of experience developing in Python.
Master’s degree in Computer Science, Computational Finance, or a directly related quantitative field.
Experience building production systems for business-critical processes.
Experience building and integrating tools, including knowledge of Gradle, Jenkins, Git, and Docker.
Experience developing with large microservice-based architectures and container orchestration frameworks.
Demonstrated experience with relational databases, including MSSQL and MySQL.
Experience writing automated unit, integration, regression, performance, and acceptance tests.
Solid understanding of software design principles.
Passion for hands-on software development.
Strong written and verbal collaboration and communication skills, including interacting with data scientists on technical topics.
Desire to continuously improve technical quality and system architecture to respond to business needs.
Ability to quickly learn, apply, and deploy new technologies to solve emerging problems.

Responsibilities

Implement models and data transformations using Python-based technologies such as PySpark and Pandas.
Implement and expose models as web service APIs using Flask, and deploy web services to AWS EKS.
Optimize Python code to reduce runtime and memory usage.
Write automated tests to validate code.
Peer review team members’ code and help with design and implementation challenges.
Build robust, scalable, and fault-tolerant systems that make models, data, and analytics available for business use.