Senior Machine Learning Engineer

Capital Group • Seattle, WA

9d • $201,683 - $322,693

About The Position

Capital Group is hiring a Senior Machine Learning Engineer to join its AI Insights team. The team builds insight systems on investment data, including multi-agent systems that answer investment questions with citations, methods for evaluating answer quality, agents that perform analyst workflows, and tools that extract reusable insight from unstructured research, calls, and filings. The role also involves recognizing when a conventional supervised model is more appropriate than a generative AI system.

The engineer will partner closely with an investment stakeholder group, learning its data, workflows, and standards for a good answer. This is applied science with a delivery focus: systems must provide reliable results for people making real investment decisions and be delivered as working products. The role is hands-on throughout, from framing problems and running experiments to prototyping and building systems. A partner engineering team owns the platform, allowing this role to focus on insight and evaluation.

The position requires strong research depth, scientific rigor, sound problem framing, applied machine learning and generative AI experience in production, and the ability to build end-to-end. The engineer will also contribute to the team through design and code reviews and mentoring. Capital Group describes a collaborative environment with working sessions, design reviews, and pair programming, and values rigor, ownership, humility, and pragmatism.

Requirements

• At least 7 years of professional experience, while remaining hands-on.
• A track record of extracting reliable signal from messy, ambiguous data and applying scientific rigor to evaluation.
• Ability to frame unfamiliar problems independently, identify core constraints, and find reusable structures rather than one-off solutions.
• First-principles problem-solving and judgment to choose the simplest effective approach rather than defaulting to a preferred tool.
• Experience taking applied machine learning and generative AI problems in production end-to-end, from data understanding and evaluation through delivery of something people use.
• Strong familiarity with AI tools, including day-to-day use of AI coding assistants and the ability to explain what has been built with them, where they helped, and where human intervention was needed.
• Clear communication of trade-offs to non-technical partners, collaborative maturity, intellectual humility, and the ability to support a direction even when it was not one's own choice.
• Ownership and experience independently driving ambiguous work to a result.
• Ability to turn ideas into working prototypes, review code critically, and guide AI coding tools toward clean results.

Nice To Haves

• Experience designing and evaluating multi-agent or tool-using systems, including understanding their failure modes.
• Experience building evaluation infrastructure, such as evaluation sets, offline and online measurement, regression detection, and drift detection.
• Finance or investment management experience, or demonstrated ability to become fluent in an unfamiliar domain quickly.

Responsibilities

• Turn underspecified requests from investment professionals into well-defined problems, including clarifying the question, what constitutes an answer, and what evidence would establish it.
• Analyze messy and incomplete data to identify genuine signal and distinguish it from leakage, lucky splits, or misleading metrics.
• Design evaluations for generative AI systems, including evaluation sets, success criteria, and appropriate use and assessment of LLM-as-judge methods.
• Run experiments to resolve team questions and document them so decisions are reproducible, including criteria set before reviewing results.
• Design and build agent systems, decomposing tasks, selecting orchestration, deciding where human review belongs, and choosing simpler deterministic or single-model approaches when appropriate.
• Build end-to-end prototypes using AI coding tools while maintaining clean, functional output.
• Take projects from an initial idea to a product people use, beginning with a short design developed with the team.
• Contribute to team quality through design and code reviews and mentoring in experimental design and rigor.
• Collaborate with investment partners and a partner engineering team to deliver insight systems and evaluations.

Benefits

• Competitive salary and individual annual performance bonus.
• Annual profitability bonus.
• Company-funded retirement plan with Capital contributing 15% of eligible earnings.
• Generous time away and health benefits from day one.
• Opportunity for flexible work options.
• Two-for-one matching gifts for charitable contributions and opportunity to secure annual grants for organizations supported by associates.
• On-demand professional development resources.
• Annual performance reviews and compensation designed to recognize and reward contributions.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed