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About This Role
General Information
Location Cary, North Carolina Alternative Location(s)
- Posting Location: New York, New York
Working Schedule Full-Time Work Arrangement Hybrid Travel Required 10% Relocation Assistance Available No Grade 13T Posted Date 28-Sep-2026 Job ID 20769
Description and Requirements
The Team You Will Join At MetLife, data isn’t just a tool – it is a catalyst for growth. As part of our Data & Analytics organization, you’ll unlock trusted insights that drive bold decisions, power personalized customer experiences, and deliver lasting business impact. We’re building the future of data – one that’s governed responsibly, engineered for scalability, and designed for growth. When you join us, you’re not just supporting the business – you’re empowering it. Let’s transform insight into impact and data into action, together.
The Opportunity The Data & AI Product Engineer is a hands-on technologist who sets the technical direction for how MetLife builds and consumes data and AI capabilities. The role defines what the enterprise needs, architects the target state from a blank page, and drives it through to production. It spans data science, data engineering and data strategy.
This is a forward-deployed role, not a portfolio or project management role. It requires the ability to take an ambiguous, high-stakes problem spanning multiple systems, owners, and constraints and reduce it to a defensible architecture and a sequenced delivery plan, then build against it hands-on. It also requires the credibility to bring that recommendation directly to executive leadership and change the decision on what MetLife should build, what it should not, and how data and AI capabilities integrate with the broader IT landscape.
Key Responsibilities
- Work with domain stakeholders to identify capability needs, assess feasibility early, and translate problems into implementable requirements.
- Help shape the technical direction for MetLife's data and AI landscape, defining the target state, the build-versus-buy position, and the sequence of investments required to get there, and keeping that direction current as the platform and vendor market shifts.
- Take on the large and ambiguous cross-cutting initiatives, such as semantic layer enablement and preparing enterprise data for frontier AI models, and bring structure to them: frame the problem, establish the decision criteria, resolve competing technical positions on evidence, and carry the work from concept through enterprise adoption.
- Architect solutions from a blank page: target-state architectures, data flow and integration diagrams, and component models precise enough to build from directly.
- Design and deliver hands-on solutions across data science and data engineering.
- Review design and code from concept through production and own the production-readiness decision.
- Guide enterprise initiatives toward the right capability, ensuring teams build on approved platforms and existing solutions rather than duplicating them, and steering misaligned approaches early.
- Serve as the trusted technical voice for data and AI with senior executive stakeholders, including the CDO, CIO, and their leadership teams. Frame trade-offs and recommendations so non-technical executives can decide with confidence, and secure the alignment and investment required to move a direction forward.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
- 5+ years of experience in data engineering, data science/AI, and data strategy, including 3+ years leading the technical direction of a platform, product, or capability.
- Proven track record delivering and operating production-grade data or AI solutions.
- Experience designing data and AI architectures, evaluating and building platforms/vendors, and making build vs. buy decisions.
- Strong hands-on skills in Python and SQL, with experience reviewing production code and validating models.
- Experience with machine learning and generative AI, including RAG and agent-based architectures.
- Deep understanding of modern data platforms, data pipelines, APIs, integrations, and cloud-based data architectures.
Preferred Qualifications
- Advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
- Regulated-environment platform experience: insurance, financial services, or another regulated industry, including platform security (access controls, PII handling, encryption, audit logging), infrastructure-as-code, and cloud cost management.
- MLOps and model governance: experiment tracking, CI/CD for models, inference patterns, drift monitoring, explainability (SHAP or equivalent), and model-risk readiness.
- Semantic layer and AI-ready data: semantic layer design and metric governance, data contracts, catalog and lineage, making enterprise data consumable by models and agents.
- Leadership and influence: holds a technical position with senior stakeholders and vendors and revises it on evidence rather than seniority; explains technical trade-offs to executive audiences without diluting them and influences across a matrixed organization without formal authority; builds durable credibility with senior executives, becoming the person leadership consults before committing to a data or AI direction; operates without a defined brief, deciding what the organization should work on and not only how to build it; raises the technical bar for peers through review, mentoring, and the architecture patterns they set.
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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 $164,600 - $213,100*. This role may also be eligible for annual short-term incentive compensation. All incentives and benefits are subject to the applicable plan terms.*
Benefits We Offer
Our U.S. benefits address holistic well-being with programs for physical and mental health, financial wellness, and support for families. We offer a comprehensive health plan that includes medical/prescription drug and vision, dental insurance, and no-cost short- and long-term disability. We also provide company-paid life insurance and legal services, a retirement pension funded entirely by MetLife and 401(k) with employer matching, group discounts on voluntary insurance products including auto and home, pet, critical illness, hospital indemnity, and accident insurance, as well as Employee Assistance Program (EAP) and digital mental health programs, parental leave, paid time off, paid holidays, volunteer time off, tuition assistance and much more! For more information regarding MetLife’s U.S. benefits, please click here.
About MetLife
Recognized on Fortune magazine's list of the "World's Most Admired Companies", Fortune World’s 25 Best Workplaces™, as well as the Fortune 100 Best Companies to Work For®, MetLife, through its subsidiaries and affiliates, is one of the world’s leading financial services companies; providing insurance, annuities, employee benefits and asset management to individual and institutional customers. With operations in more than 40 markets, we hold leading positions in the United States, Latin America, Asia, Europe, and the Middle East.
As part of our New Frontier strategy, MetLife is building an AI-enabled, people-centered future. We’re looking for people who bring curiosity, adaptability, and a growth mindset as we use AI to enhance how we serve customers, support communities, and evolve the way work gets done. At MetLife, AI is a responsible partner that supports human judgment, creativity, and continuous improvement while helping us build trust, inclusion, and long-term value.
Our purpose is simple - to help our colleagues, customers, communities, and the world at large create a more confident future. United by purpose and guided by our core values - Win Together, Do the Right Thing, Deliver Impact Over Activity, and Think Ahead - we’re inspired to transform the next century in financial services. At MetLife, it’s #AllTogetherPossible. Join us!
MetLife is an Equal Opportunity Employer. All employment decisions are made without regards to race, color, national origin, religion, creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, marital or domestic/civil partnership status, genetic information, citizenship status (although applicants and employees must be legally authorized to work in the United States), uniformed service member or veteran status, or any other characteristic protected by applicable federal, state, or local law (“protected characteristics”).
If you need an accommodation due to a disability, please email us at [email protected]. This information will be held in confidence and used only to determine an appropriate accommodation for the application process.
MetLife maintains a drug-free workplace. This posting is for a current vacancy and is anticipated to remain open for at least 90 days from the listed posting date.
Salary Context
This $164K-$213K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($188K) sits 11% below the category median. Disclosed range: $164K to $213K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
MetLife AI Hiring
MetLife has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Cary, NC, US, Bridgewater, NJ, New York, NY, US. Compensation range: $75K - $213K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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