Senior Machine Learning Engineer

The Senior Machine Learning Engineer role on Capital Group’s AI Insights team focuses on building an insight layer over investment data using multi-agent and generative AI systems. The work emphasizes rigorous evaluation and practical delivery, transforming unstructured research and other inputs into reusable, decision-supporting insights validated against evidence and defined performance criteria.

Responsibilities

• Refine an underspecified request into a solvable problem by clarifying what is being asked, what constitutes an answer, and what evidence would resolve the question.
• Extract signal from messy, incomplete data, distinguishing genuine results from leakage, lucky splits, or misleading metrics.
• Develop evaluation plans for Generative AI systems, including evaluation sets, success criteria, LLM-as-judge approaches, known failure modes, and judgment mechanisms that ensure measured numbers reflect intended behavior.
• Run experiments that settle team debates and write results for reproducibility, including criteria committed to before reviewing outcomes.
• Design and build agent systems that generate insight by decomposing tasks, selecting orchestration, defining where a human belongs in the loop, and recognizing when simpler deterministic steps or single model calls are the most appropriate solution.
• Build end-to-end prototypes, leveraging AI coding tools to move quickly while keeping outputs clean and functional.
• Take ideas through early design to usable deliverables, starting with short designs shaped together with the team.
• Improve team craft through design and code review, along with mentoring on experimental design and research rigor.

Requirements

• Research depth and scientific rigor: experience extracting real signal from ambiguous data, designing strong evaluations, and challenging results that appear too strong.
• Abstraction and problem framing: ability to identify core constraints in unfamiliar problems without handholding and to build reusable structures rather than one-off solutions.
• First-principles problem solving: ability to begin with the problem and its constraints rather than a preferred tool, and to pursue the simplest approach that works.
• Applied ML and Generative AI in production: end-to-end ownership from data understanding through evaluation to something people actually use.
• AI acumen: capacity to learn new tools by understanding how they work, effective day-to-day collaboration with AI coding assistants, and the ability to describe what was built, where the assistants helped, and where human intervention was required.
• Communication, collaboration, and maturity: ability to explain trade-offs to non-technical partners, comfort admitting “I don’t know,” fair presentation of disagreements, and the ability to strengthen others; willingness to support a direction you did not initially choose.
• Ownership: drive ambiguous efforts to a defined result independently.
• Builder judgment: 7+ years of professional experience with continued hands-on involvement; ability to turn ideas into working prototypes, review code with taste, steer AI coding tools toward clean results, and focus on making research real rather than algorithmic puzzles.

Preferred

• Designing and evaluating multi-agent or tool-using systems, including a clear understanding of where they fail.
• Building evaluation infrastructure such as eval sets, offline and online measurement, regression and drift detection.
• Finance or investment management background, or demonstrated ability to become fluent in an unfamiliar domain quickly.

Location and Work Model

Location: Los Angeles, CA (hybrid).

Compensation

Salary range: USD 201,683 - 322,693 per year.

Base salary range (Southern California): $201,683-$322,693.

Benefits

• Generous time-away and health benefits from day one, with flexible work options.
• 2-for-1 matching gifts for charitable contributions, plus the opportunity to secure annual grants for organizations you support.
• On-demand professional development resources to hone existing skills and learn new ones.
• Competitive salary, bonuses and benefits.
• Company-funded retirement contribution that factors in salary and variable pay, including bonuses.
• Individual annual performance bonus.
• Capital’s annual profitability bonus.
• Retirement plan where Capital contributes 15% of your eligible earnings.

How We Work, and What We Value

• Nobody is keeping score.
• Disagreement stays focused on the work rather than the person, and influence is not tied to volume.
• A significant portion of the week is spent in working sessions, including brainstorming, design review, and pair programming.
• Problems are genuinely ambiguous, and not every effort results in success.
• Rigor: you attempt to break your own results before others do.
• Ownership: you act as a driver rather than a passenger.
• Humility: a stronger argument can change your mind.
• Pragmatism: you know when a rough answer is acceptable and when the work must be airtight.