Machine Learning Engineer II

Capital Group•Los Angeles, CA

1d•$159,354 - $254,966•Hybrid

About The Position

As a Machine Learning Engineer at Capital Group, you will create, research, implement, and maintain state of the art machine learning models, data pipelines, and analytical systems to significantly enhance our investment processes and outcomes. You will conduct applied research into financial modeling to help our investment professionals make better decisions. In this role, you will collaborate directly with senior investment professionals and fellow technology associates to enhance our investment process through the use of state-of-the art ML techniques. In this role you will work with a high performing team of applied scientists, machine learning engineers, and software development engineers that has delivered a number of AI/ML systems to production, including machine learning systems, data pipelines, financial models, and generative AI systems. We are seeking a candidate who has hands-on experience architecting and delivering working systems and who is passionate about leveraging modern machine learning and software engineering innovations to produce superior long-term investment outcomes.

Requirements

3+ years of experience with Python and SQL.
Strong object-oriented or functional design skills with understanding of common design patterns.
A demonstrated track record in one or more machine learning subfield relevant to financial modeling, such as time series analysis, quantitative modeling, optimization, anomaly detection, or predictive analytics.
Experience solving “full stack” machine learning problems, from data collection and ETL development to model training and deployment, and experience using machine learning to solve real business problems in finance or investment management.
Strong communicator with the ability to establish and maintain a close working relationship with distributed team members and business partners.
Strong computer science fundamentals including data structures, algorithms, and complexity analysis.
Knowledge of software engineering best practices (e.g. Agile software development, test-driven development, unit testing, code reviews, design documentation).
A track record of successfully delivering enterprise-grade models into production.

Nice To Haves

Experience with AWS services (S3, containers – ECS & EKS, Lambda) preferred.

Responsibilities

Design, research, build, deliver and operate machine learning systems, data pipelines, and financial models which demonstrably improve the efficiency and effectiveness of CG’s investment process by making investment decisions.
Successfully research, train, evaluate and deploy models for financial modeling and data analysis from inception to deployment and operation.
Uphold a high standard of quality in your work, ensuring integrity in both form and function.
Write clear, efficient, and performant code.
Collaborate effectively to support team strategy, contributing to decisions regarding modeling and technology.
Proactively identify and tackle the root causes of endemic modeling problems, collaborating with cross-functional teams to implement sustainable solutions.
Work with a sense of urgency, and design and build simple and pragmatic solutions which solve complex problems.