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Join our Global Risk Management Group, where you’ll play a key role in safeguarding the company, ensuring we deliver on our commitments to customers and stakeholders, and driving responsible growth. As part of our team, you’ll identify, monitor, and mitigate both financial and non\-financial risks across the organization. Leverage your expertise as the second line of defense to advise our business on effective risk management strategies. This means challenging ideas, implementing robust controls, and enforcing guardrails to foster responsible business growth. Join us in advancing MetLife’s legacy of trust through exemplary risk management practices.
The Vice President, AI Risk \& Governance, is a strategic second\-line risk leadership role within Global Risk Management (GRM) responsible for maintaining and evolving MetLife’s AI risk and governance framework.
The role identifies novel and emerging risks presented by artificial intelligence, translates those risks into governance requirements and practical controls, and ensures the enterprise’s AI activities remain aligned to risk appetite, existing financial and non\-financial risk governance frameworks and regulatory expectations.
This position has broad enterprise impact through close collaboration within GRM, Data \& Analytics, Technology, Legal, Privacy, Information Security, Ethical AI, Data Governance, Model Risk and Business stakeholders. The role helps shape the strategic vision for AI risk management, drives risk\-based decisioning in the AI approval process, and strengthens consistency, transparency, and scalability of governance across new and emerging AI initiatives.
The role is critical to enabling AI development and innovation responsibly by balancing business value creation with disciplined risk oversight, clear escalation of material and emerging issues, and ongoing monitoring of AI risk themes, control effectiveness, and emerging regulatory developments.
- Lead day\-to\-day execution of the AI governance approval process, including risk\-based review, challenge, escalation, and alignment to enterprise risk appetite and responsible AI principles.
- Provide credible second\-line challenge and thought leadership to senior stakeholders on acceptable use, control requirements, and mitigation strategies for AI risks.
- Translate risk appetite and regulatory expectations into actionable governance requirements and control expectations.
- Identify and assess novel, emerging, and cross\-cutting risks arising from AI.
- Drive strategic improvements to financial and non\-financial AI risk and governance processes, including transparency, efficiency, operating model design, and reporting.
- Help implement the strategic vision for AI risk management, including scalable governance, stronger risk identification, and monitoring capabilities.
- Partner with control functions and business teams to embed effective controls across the AI lifecycle.
- Operationalize risk\-based integration across governance processes at the workflow and control level.
- 12\+ years’ risk management experience within financial services or regulated industry.
- Deep understanding of AI technologies and associated risks, including machine learning, generative AI, agentic AI, model risk, and data risk, along with their application to business processes and systems.
- Proven experience designing, implementing, and enhancing enterprise governance frameworks, policies, standards, and approval processes.
- Demonstrated success operating within complex, highly matrixed organizations and influencing senior stakeholders across functions.
- Exceptional strategic thinking, judgment, problem\-solving, and executive communication skills with the ability to identify emerging and regulatory issues and translate them into practical governance actions.
- Strong knowledge of regulatory, governance, and control expectations related to AI, models, data, and responsible AI practices.
- Bachelor’s degree required.
- Advanced degree in data science, applied mathematics, AI/ML, actuarial science or a related field preferred.
Location Expectation: This is a hybrid role requiring a minimum of 3 days per week in office.
*The expected salary range for this position is$238,800 \- $318,300. This role may also be eligible for annual short\-term incentive compensation and stock\-based long\-term incentives. All incentives and benefits are subject to the applicable plan terms.*
Salary Context
This $238K-$318K 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 MetLife, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($278K) sits 27% above the category median. Disclosed range: $238K to $318K.
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.
MetLife AI Hiring
MetLife has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Cary, NC, US. Compensation range: $161K - $318K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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