Machine Learning Engineer II

Company Overview: Capital Group is one of the world's largest and most established investment management firms, founded in Los Angeles in 1931 with over $3 trillion in assets under management. The firm operates globally across equity, fixed income, and multi-asset strategies, serving long-term investors through a distinctive analyst-driven research model. Capital Group is privately held and employee-owned, with a culture that prioritizes intellectual rigor, collaboration, and long-term thinking over short-term performance pressure. Role Overview: The Machine Learning Engineer will create, research, implement, and maintain state of the art machine learning models, data pipelines, and analytical systems to significantly enhance Capital Group's investment processes and outcomes. This person will conduct applied research into financial modeling to help investment professionals make better decisions, collaborating directly with senior investment professionals and technology associates to enhance the investment process through the use of state of the art ML techniques. This role sits within 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. Capital Group is 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. What you'll do: Design, research, build, deliver and operate machine learning systems, data pipelines, and financial models that demonstrably improve the efficiency and effectiveness of CG's investment decision making process. Research, train, evaluate and deploy models for financial modeling and data analysis from inception through 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 on modeling and technology. Proactively identify and tackle the root causes of endemic modeling problems, working with cross-functional teams to implement sustainable solutions. Work with a sense of urgency, designing and building simple, pragmatic solutions to complex problems. Qualifications: 3+ years of experience with Python and SQL, with strong object-oriented or functional design skills and understanding of common design patterns. A demonstrated track record in one or more ML subfields relevant to financial modeling, such as time series analysis, quantitative modeling, optimization, anomaly detection, or predictive analytics. Strong quantitative finance and backtesting skills, or extreme high-performance, large-scale computing experience. Candidates with both are ideal; if only one, quant finance and backtesting is strongly preferred. Experience solving "full stack" machine learning problems, from data collection and ETL development through model training and deployment, with a track record of using ML to solve real business problems in finance or investment management. Strong communication skills, with the ability to establish and maintain close working relationships 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 development, test-driven development, unit testing, code reviews, design documentation). A track record of successfully delivering enterprise grade models into production. Experience with AWS services (S3, containers, ECS and EKS, Lambda) preferred. Southern California Base Salary Range: $159,354 to $254,966 Seattle Base Salary Range: $159,354 to $254,966