Interested in this AI/ML Engineer role at MetLife?
Apply Now →Skills & Technologies
About This Role
General Information
Location Cary, North Carolina Working Schedule Full-Time Work Arrangement Hybrid Relocation Assistance Available No Grade 08T Posted Date 06-Oct-2026 Job ID 20496
Description and Requirements
The Team You Will Join When you join MetLife’s Global Technology team, you’ll be part of a forward-thinking group dedicated to shaping the future of digital solutions for customers worldwide. You’ll develop, maintain and support technology applications and delivery, leveraging AI, automation, and contemporary ways of working to enhance experiences and drive business outcomes. Your work will simplify complex processes, improve tech resiliency, and ensure high-performing, seamless solutions that power life’s most important moments. In this dynamic environment, you’ll collaborate with talented peers across teams and functions, expanding your skills in impactful ways. Ready to push boundaries and set new industry standards? Join us and help drive the future of technology forward.
The Opportunity The Jr. AI Engineer is part of the Corporate Technology AI Accelerator, helping build, test and support reusable AI capabilities that enable corporate functions and business teams to solve meaningful problems at scale. Working under the direction of senior engineers, you will contribute to enterprise agents, knowledge retrieval, integration and automation while learning the common engineering patterns, governance and responsible-AI practices that help solutions move from idea to secure, reliable production. Guided by our purpose — always with you, building a more confident future — and our New Frontier strategy focused on stronger growth, attractive returns and all-weather performance, this is an opportunity to begin an engineering career on real enterprise AI, collaborate across product, platform and business teams, and help turn AI-first ambition into trusted employee experiences and measurable business impact.
Key Responsibilities
- + Develop AI-enabled application components, integrations and automation logic under the direction of senior engineers, using Python and C#/.NET.
- + Support implementation of retrieval-augmented workflows, including embeddings, vector search, Azure AI Search indexes and prompt configuration.
- Assist in building employee-facing conversational experiences delivered through Microsoft Teams, including Adaptive Cards and message extensions.
- + Build and test REST API integrations between AI components and enterprise HR, workplace and line-of-business systems.
- + Contribute to deployment tasks, operational checks and Infrastructure as Code activities for AI workloads, using established patterns and templates.
- + Test and validate model and prompt outputs by applying grounding checks, guardrails and quality standards, escalating responsible-AI questions promptly.
- + Capture telemetry and support basic observability and response-quality reporting for deployed agents.
- Maintain documentation for prompts, workflows, tools and AI system behavior, and participate in Agile ceremonies within the engineering team.
- Learn and apply approved AI platform standards, model integration practices, responsible AI guidance and human-in-the-loop design patterns.
Required Qualifications
- Working knowledge of Python and/or C# (.NET), or another major programming language such as JavaScript, TypeScript or Java, with sound software engineering fundamentals.
- Foundational proficiency with generative AI: an understanding of large language models, embeddings and prompt-based development, together with the judgment to review AI-generated output critically and responsibly rather than accept it at face value.
- Willingness to work under direct guidance, act on feedback, and follow established engineering, security, privacy and responsible-AI standards.
- Analytical and problem-solving ability, with a demonstrated learning mindset.
- Clear written and verbal communication, and the ability to collaborate with engineering, product and business partners and to raise blockers early.
Preferred Qualifications
- Suggested 0–1 years of experience in AI, machine learning or software engineering involving large language models, including internships, academic projects or equivalent practical work.
- Exposure to enterprise agent development, or to Microsoft 365 and Microsoft Teams application development.
- Familiarity with the Microsoft agent stack, including the Microsoft Agent Framework, the Microsoft 365 Agent SDK, Azure AI Search or Azure AI Foundry.
- Exposure to retrieval-augmented generation, vector search, or Model Context Protocol tool integrations.
- Exposure to cloud platforms, Azure preferred, and to Infrastructure as Code using Terraform.
- Awareness of observability, telemetry and AI response-quality measurement.
- Experience using GitHub Copilot or a similar AI coding assistant.
- Azure fundamentals certification (AZ-900 or AI-900).
- Bachelor's degree in Computer Science, AI, machine learning, data science, information systems, engineering or a related field, or equivalent practical experience, applied to software or AI engineering.
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 55,800 - 75,000. 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 $55K-$75K range is in the lower quartile 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. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($65K) sits 69% below the category median. Disclosed range: $55K to $75K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.