Interested in this AI/ML Engineer role at Corebridge?
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Who We Are
At Corebridge Financial, we believe action is everything. That’s why every day we partner with financial professionals and institutions to make it possible for more people to take action in their financial lives, for today and tomorrow.
We align to a set of Values that are the core pillars that define our culture and help bring our brand purpose to life:
- We are stronger as one: We collaborate across the enterprise, scale what works and act decisively for our customers and partners.
- We deliver on commitments: We are accountable, empower each other and go above and beyond for our stakeholders.
- We learn, improve and innovate: We get better each day by challenging the status quo and equipping ourselves for the future.
- We are inclusive: We embrace different perspectives, enabling our colleagues to make an impact and bring their whole selves to work.
Who You’ll Work With:
The Information Technology (IT) organization is the technological foundation of our business and works in collaboration with our partners from across the company. The team drives technology and digital transformation and partners with business leaders to design and execute new strategies across the company. They also ensure the necessary IT risk management and security measures are in place and aligned with enterprise architecture standards and principles.
About The Role:
The Director of AI Security will own the enterprise’s AI Security Standard and the technical security program that operationalizes it — designing, implementing, and governing the controls that ensure the organization builds and consumes artificial intelligence (including generative AI, agentic AI, and third-party/embedded AI capabilities) securely and in compliance with emerging regulatory expectations.
Reporting to the Head of Cyber Data Protection and AI Security, this role works in close, continuous partnership with the Enterprise AI team , which owns the overarching AI Strategy and AI Standard (governing acceptable use, business adoption, and enterprise AI investment). The Director of AI Security ensures that standard is backed by a rigorous, enforceable security layer — covering model risk, data protection, identity, vulnerability management, and attack-path/threat modeling across the AI lifecycle, from model selection and data sourcing through deployment, monitoring, and decommissioning.
This is a hands-on leadership role for someone who can operate simultaneously at the standards/control-framework level (audit- and board-ready documentation) and the technical control level, while building a trusted, collaborative relationship with a peer team that owns adjacent AI governance.
Responsibilities:
AI Security Standard Ownership & Cross-Functional Alignment
- Ensure the AI Security Standard is fully aligned with, and traceable to, the Enterprise AI team’s AI Strategy and AI Standard — acting as the primary security liaison and co-author on any sections where the two documents intersect (approved use cases, data handling, model sourcing).
- Partner with the Enterprise AI team to define and maintain the security review criteria feeding the Approved AI/Model List, including risk-tiering methodology for AI types (traditional ML, generative AI, agentic AI, embedded/third-party AI) and a joint exception/waiver process.
- Co-develop Appropriate Use Policy (AUP) security requirements with the Enterprise AI team, ensuring employee-, developer-, and business-unit-facing guidance reflects both AI Standard intent and AI Security Standard controls.
- Map AI security requirements to relevant regulatory and industry frameworks (e.g., NIST AI RMF, ISO/IEC 42001, EU AI Act, OWASP Top 10 for LLMs, MITRE ATLAS) and translate them into the internal control requirements embedded in the AI Security Standard.
- Establish a standing governance cadence (working group, review board, or joint steering sync) with the Enterprise AI team to keep the AI Standard and AI Security Standard synchronized as AI adoption scales and regulations evolve.
AI Security Architecture & Risk Management
- Lead security risk assessments for AI/ML pipelines, generative AI deployments, and agentic AI systems, addressing risks such as prompt injection, data leakage, model poisoning, excessive agency, insecure tool/plugin integration, and supply chain risk in third-party models.
- Partner with data protection, identity, and vulnerability management teams to extend existing enterprise security programs into AI-specific contexts — including access controls for model endpoints, data classification for training/inference data, and secure API/agent orchestration.
- Own attack path and threat modeling for AI assets — identifying novel and indirect attack paths introduced by AI integrations across applications, devices, and data sources, and driving remediation prioritization.
- Evaluate and secure agent creation platforms (e.g., Copilot Studio, custom agent frameworks) used within regulated business functions, ensuring guardrails, logging, and human-in-the-loop controls are enforced.
Program & Cross-Functional Leadership
- Serve as the primary security stakeholder in AI procurement, vendor risk assessments, and build-vs-buy decisions for AI capabilities.
- Partner with Legal, Privacy, Compliance, and Data Governance teams to ensure AI initiatives meet regulatory obligations (data privacy, sector-specific regulation, third-party risk).
- Build and lead a small team (or dotted-line working group) of AI security engineers/analysts as the program scales. Utilize support from MSSP and specialized contractors as the team scales.
- Report AI security posture, control maturity, and incident trends to the Head of Cyber Data Protection and AI Security, and support executive/board-level reporting as needed.
- Lead incident response planning and tabletop exercises specific to AI security failure modes (model misuse, data exfiltration via AI tools, agent misbehavior).
Enablement & Culture
- Develop training and awareness programs to help engineering, product, and business teams understand and apply the AI Security Standard in coordination with Enterprise AI’s broader AI Standard training.
- Act as an internal advisor/enabler — helping teams adopt AI safely rather than acting purely as a gatekeeper.
Track the token/compute cost and risk-remediation ROI of using frontier AI models for security use cases (e.g., automated vulnerability triage, code remediation) to inform build decisions.
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Skills and Qualifications:
- 10+ years in cybersecurity, with at least 3–5 years focused on data protection, cloud security, or emerging technology risk; demonstrated ownership of a formal security standard or governance framework.
- Direct experience authoring or operationalizing AI security standards (or directly transferable experience in data protection/data governance standards) at an enterprise level, including experience partnering with an adjacent team that owns broader AI strategy/policy.
- Strong understanding of AI/ML lifecycle risks, generative AI security concerns, and emerging agentic AI risk patterns.
- Familiarity with relevant frameworks: NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, MITRE ATLAS, EU AI Act, NIST 800-53/CSF.
- Experience with Microsoft security/identity stack and/or vulnerability management platforms (e.g., Qualys VMDR) as applied to AI asset inventory and control coverage.
- Experience with AI Security platforms (e.g. Varonis Atlas) to discover and monitor AI usage, evaluate AI interactions, perform security testing of AI models and collect compliance events data related to AI usage.
- Experience in regulated industries (financial services, healthcare, defense, critical infrastructure) and ability to translate regulatory requirements into technical/administrative controls.
- Demonstrated ability to build effective, collegial working relationships across peer teams with adjacent but distinct ownership (e.g., co-authoring standards, joint governance boards) without formal authority over those teams.
Bachelor’s degree or equivalent experience; relevant certifications a plus (CISSP, CCSP, AI governance certifications, etc.).
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Preferred Qualifications:
- Experience building or contributing to a risk intelligence / GRC platform that aggregates vulnerability, asset, and attack-path data across applications, devices, and people.
- Experience with M&A security due diligence, including assessing AI/data risk in acquired entities.
- Background in OT/ICS or classified/high-assurance environments.
- Experience configuring or evaluating agent orchestration platforms (e.g., Microsoft Copilot Studio) for security and compliance.
- Hands-on experience building and deploying AI applications, including generative AI or agent-based solutions.
Demonstrated experience automating security assessments and operational processes through workflow platforms, scripting, or AI-enabled solutions, with measurable improvements in speed, quality, or control effectiveness.
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Compensation
Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered will ultimately be dependent on multiple factors, which may include the candidate’s geographic location, skills, experience and other qualifications.
In addition, the position is eligible for a discretionary bonus in accordance with the terms of the applicable incentive plan.
Corebridge also offers a range of competitive benefits as part of the total compensation package, as detailed below.
Work Location
This position is based in Corebridge Financial’s Houston, TX office and is subject to our hybrid working policy, which gives colleagues the benefits of working both in an office and remotely.
Open to considering remote candidates.
#LI-RL1 #LI-SAFG #LI-Hybrid #LI-Remote
This role is deemed a “covered associate” under SEC Rule 206(4)-5, 17 CFR § 275.206(4)-5, Political contributions by certain investment advisers, and other federal and state pay-to-play rules. Candidates for the role must not have made any political contributions that, under 17 CFR § 275.206(4)-5 or other federal or state pay-to-play regulations, would disqualify the candidate or Corebridge Financial from conducting Corebridge Financial’s business, or that would otherwise create a conflict of interest for Corebridge Financial. Applicants who are selected to move forward with the application process will be required to disclose all U.S. political contributions they and their household family members have made over the past two years. Why Corebridge?
At Corebridge Financial, we prioritize the health, well-being, and work-life balance of our employees. Our comprehensive benefits and wellness program is designed to support employees both personally and professionally, ensuring that they have the resources and flexibility needed to thrive.
Benefit Offerings Include:
- Health and Wellness: We offer a range of medical, dental and vision insurance plans, as well as mental health support and wellness initiatives to promote overall well-being.
- Retirement Savings: We offer retirement benefits options, which vary by location. In the U.S., our competitive 401(k) Plan offers a generous dollar-for-dollar Company matching contribution of up to 6% of eligible pay and a Company contribution equal to 3% of eligible pay (subject to annual IRS limits and Plan terms). These Company contributions vest immediately.
- Employee Assistance Program: Confidential counseling services and resources are available to all employees.
- Matching charitable donations: Corebridge matches donations to tax-exempt organizations 1:1, up to $5,000.
- Volunteer Time Off: Employees may use up to 16 volunteer hours annually to support activities that enhance and serve communities where employees live and work.
Paid Time Off: Eligible employees start off with at least 24 Paid Time Off (PTO) days so they can take time off for themselves and their families when they need it. *
Eligibility for and participation in employer-sponsored benefit plans and Company programs will be subject to applicable law, governing Plan document(s) and Company policy.
We are an Equal Opportunity Employer
Corebridge Financial, is committed to being an equal opportunity employer and we comply with all applicable federal, state, and local fair employment laws. All applicants will be considered for employment based on job-related qualifications and without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, disability, neurodivergence, age, veteran status, or any other protected characteristic. The Company is also committed to compliance with all fair employment practices regarding citizenship and immigration status. At Corebridge Financial, we believe that diversity and inclusion are critical to building a creative workplace that leads to innovation, growth, and profitability. Through a wide variety of programs and initiatives, we invest in each employee, seeking to ensure that our colleagues are respected as individuals and valued for their unique perspectives.
Corebridge Financial is committed to working with and providing reasonable accommodations to job applicants and employees, including any accommodations needed on the basis of physical or mental disabilities or sincerely held religious beliefs. If you believe you need a reasonable accommodation in order to search for a job opening or to complete any part of the application or hiring process, please send an email to [email protected] . Reasonable accommodations will be determined on a case-by-case basis, in accordance with applicable federal, state, and local law.
We will consider for employment qualified applicants with criminal histories, consistent with applicable law.
To learn more please visit: www.corebridgefinancial.com
Functional Area:
IT - Information Technology Estimated Travel Percentage (%): Up to 25% Relocation Provided: No American General Life Insurance Company
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Corebridge, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Corebridge AI Hiring
Corebridge has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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