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About This Role
“I can be myself at work.”
You are more than a job title. We want you to feel comfortable doing great work and bringing your best, authentic self to everything you do. We value your talents, traditions, and uniqueness—and we’re committed to fostering a strong sense of belonging in a respectful workplace.
We intentionally seek diverse perspectives, experiences, and backgrounds, investing in a culture designed to celebrate differences. We believe that belonging leads to better outcomes and a stronger community of associates united by our mission. At Capital, we live our core values every day: Integrity, Client Focus, Diverse Perspectives, Long\-Term Thinking, and Community.
“I can influence my income.”
You want to feel recognized at work. Your performance will be reviewed annually, and your compensation will be designed to motivate and reward the value that you provide. You’ll receive a competitive salary, bonuses and benefits. Your company\-funded retirement contribution will factor in salary and variable pay, including bonuses.
“I can lead a full life.”
You bring unique goals and interests to your job and your life. Whether you’re raising a family, you’re passionate about where you volunteer, or you want to explore different career paths, we’ll give you the resources that can set you up for success.
- Enjoy generous time\-away and health benefits from day one, with the opportunity for flexible work options
- Receive 2\-for\-1 matching gifts for your charitable contributions and the opportunity to secure annual grants for the organizations you love
- Access on\-demand professional development resources that allow you to hone existing skills and learn new ones
“I can succeed as a AI Engineer Lead at Capital Group.”
As a AI Engineer Lead, you'll partner with investment professionals and business partners to turn ambiguous problems into working AI solutions that improve our investment process and business outcomes. You are the build\-and\-deploy bridge between the people who own the problem and the AI platform that powers the answer: you lead discovery, design the approach, write the code, and own it in production. This is a hands\-on engineering role for someone who is as comfortable in a working session with a business team as they are building a retrieval pipeline or hardening an agent. You will help set the standard for how generative AI gets built and operated responsibly at scale across the firm.
### You will
- Partner directly with business partners to understand their workflows, scope the highest\-value opportunities, and translate ambiguous needs into clear technical specifications.
- Design, build, and operate production generative AI applications — copilots, assistants, knowledge\-search experiences, and agentic workflows — as reliable, production\-grade systems rather than demos.
- Architect and implement end\-to\-end retrieval\-augmented generation pipelines, including parsing, ingestion, chunking strategy, embeddings, vector storage, retrieval, and prompt management.
- Build agents and agentic workflows that plan and execute multi\-step tasks within explicit, auditable boundaries, with guardrails that keep behavior safe and predictable.
- Practice eval\-driven development: define acceptance criteria up front, build evaluation harnesses, and measure correctness, latency, and hallucination so quality is verifiable and regressions are caught before production.
- Take end\-to\-end ownership from discovery and design through build, rollout, and operational excellence — instrumenting systems with the observability, cost tracking, and audit trails needed to know when they degrade.
- Apply FinOps and cost\-optimization practices to AI workloads, tracking and managing token, inference, and infrastructure spend so solutions stay cost\-effective as they scale.
- Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed\-systems judgment.
- Apply responsible\-AI judgment proportionate to the risk of each use case, working with risk and compliance partners to build the controls, human\-oversight patterns, and audit trails that let the firm move quickly and safely.
- Embed security, privacy, and compliance controls into the systems you build — including identity and access management (IAM), encryption, and audit logging — partnering with InfoSec and data\-governance teams to meet regulatory and internal\-policy requirements such as SOC 2 and applicable data\-privacy regulations.
- Codify what works into reusable tools, patterns, and playbooks, and feed insights back to platform, product, and engineering partners so the whole organization gets faster.
- Produce clear documentation, runbooks, and architectural diagrams so the systems you build can be understood, operated, and extended by others.
“I am the person Capital Group is looking for.”
### Required qualifications
- Minimum 10\+ years of professional software engineering experience, with strong proficiency in Python (or a comparable modern language).
- Hands\-on production experience building and shipping LLM\-powered applications, including advanced prompt engineering, retrieval, agent development, and evaluation.
- Demonstrated experience designing and building end\-to\-end RAG pipelines and integrating LLM solutions with real systems.
- Strong understanding of system design, APIs, distributed\-systems concepts, and cloud\-native development, with a track record of owning production systems on solid architectural foundations.
- A disciplined approach to evaluation and testing for non\-deterministic systems — you build evals and guardrails as a first\-class part of the work, not an afterthought.
- Strong communication skills: you can lead technical discovery, write clearly, and convey technical concepts to mixed audiences while keeping a low ego and a collaborative approach.
- High agency and comfort navigating the ambiguity of a large, regulated organization, with the judgment to make trade\-offs between scope, speed, and quality.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- Experience implementing security, privacy, and compliance controls in production systems — for example IAM, encryption, audit logging, and data\-governance practices, ideally in a regulated environment.
“I can apply in less than 4 minutes.”
You’ve reviewed this job posting and you’re ready to start the candidate journey with us. Apply now to move to the next step in our recruiting process. If this role isn’t what you’re looking for, check out our other opportunities and join our talent community.
“I can learn more about Capital Group.”
At Capital Group, the success of the people who invest with us depends on the people in whom we invest. That’s why we offer a culture, compensation and opportunities that empower our associates to build successful and prosperous careers. Through nine decades, our goal has been to improve people’s lives through successful investing. We know that our history is a testament to the strength of the people we hire. More than 9,000 associates in 30\+ offices around the world help our clients and each other grow and thrive every day. Find us on LinkedIn, Instagram, YouTube and Glassdoor.
Southern California Base Salary Range: $179,273\-$286,837
*In addition to a highly competitive base salary, per plan guidelines, restrictions and vesting requirements, you also will be eligible for an individual annual performance bonus, plus Capital’s annual profitability bonus plus a retirement plan where Capital contributes 15% of your eligible earnings.*
*You can learn more about our compensation and benefits* *here**.*
- *Temporary positions in the United States are excluded from the above mentioned compensation and benefit plans.*
*We are an equal opportunity employer, which means we comply with all federal, state and local laws that prohibit discrimination when making all decisions about employment. As equal opportunity employers, our policies prohibit unlawful discrimination on the basis of race, religion, color, national origin, ancestry, sex (including gender and gender identity), pregnancy, childbirth and related medical conditions, age, physical or mental disability, medical condition, genetic information, marital status, sexual orientation, citizenship status, AIDS/HIV status, political activities or affiliations, military or veteran status, status as a victim of domestic violence, assault or stalking or any other characteristic protected by federal, state or local law.*
Salary Context
This $179K-$286K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Capital Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($233K) sits 7% above the category median. Disclosed range: $179K to $286K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Capital Group AI Hiring
Capital Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $286K - $286K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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