Senior Machine Learning Engineer

Capital Group•Los Angeles, CA

14d•$201,683 - $322,693•Hybrid

About The Position

We are seeking a strong software engineer with practical machine learning experience to design, build, and operate Generative AI systems that improve Capital Group’s investment process and outcomes. You will work closely with investment professionals and engineering partners to translate ideas into reliable, production-grade systems. While curiosity and experimentation are important, this role emphasizes engineering rigor, system design, and delivering durable solutions at scale. You will join a high-performing team of ML engineers and software engineers building Generative AI and LLM-based capabilities, including multi-agent systems, semantic search, summarization, and document understanding pipelines. We value craftsmanship in software engineering, thoughtful problem solving, and a strong sense of ownership.

Requirements

7+ years of professional software engineering experience, with strong proficiency in Python (or similar modern language).
Demonstrated experience designing and building large scale production systems with solid architectural foundations.
Strong understanding of system design principles, APIs, distributed systems concepts, and cloud-native development.
Hands-on experience integrating machine learning models into production applications or services
Experience with cloud platforms, CI/CD, DevOps, or MLOps practices for deploying and monitoring applications.
Strong problem-solving ability — able to break down ambiguous problems into clear technical plans.
Proven ability to own projects independently and drive them to completion.
Strong communication skills and behavioral maturity; able to collaborate effectively with diverse stakeholders.

Nice To Haves

Experience working with Generative AI, LLM APIs, retrieval systems, or agent-based architectures.
Experience with ML lifecycle tooling such as experiment tracking, model versioning, monitoring, or CI/CD for ML systems
Familiarity with model evaluation, monitoring, and performance trade-offs.
Exposure to modern ML frameworks (e.g., PyTorch, HuggingFace, etc.).
Experience reviewing research papers and translating relevant ideas into practical implementations.

Responsibilities

Design and implement scalable, production-quality systems that incorporate machine learning and Generative AI components.
Take end-to-end ownership of technical projects, from system design through implementation and operational excellence.
Collaborate with cross-functional partners to translate ambiguous problems into well-structured technical solutions.
Maintain a high bar for code quality, testing, clarity, and reliability.
Contribute to architectural decisions, ensuring systems are maintainable, extensible, and resilient.
Apply sound engineering judgment when integrating ML models into larger distributed systems.
Operate with urgency, ownership, humility, and strong collaboration across teams.