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About This Role
This role sits at the intersection of human centered design, enterprise AI strategy, and organizational change, with a clear mandate: prove how AI should work for people at Chubb, then scale it across the enterprise. Chubb is building AI infrastructure that will fundamentally change how work gets done. We are moving toward a future where employees can query decades of institutional knowledge, generate documents grounded in organizational context, and surface insights from emails, meetings, and internal systems through natural, conversational interaction with AI.
The VP, Human–AI Experience \& Adoption exists to make that future real. You will serve as the connector among users, technologists, and senior leaders across Global Data \& Analytics, advancing the vision, building early examples, and driving adoption of AI native productivity tools. This is not a theoretical or advisory role. You will design, build, deploy, and iterate, demonstrating what’s possible before scaling solutions across the enterprise. Our credibility and product insight depend on being exemplary AI users ourselves. This role exists to break through institutional inertia, challenge “the way we have always done it” and make our AI\-team the most AI\-augmented team at Chubb.
Enterprise AI Strategy \& Adoption Leadership
- Identify where AI can materially improve how people work, prioritizing impact over experimentation
- Partner with Data \& Analytics leadership to define AI adoption roadmaps grounded in real user needs
- Lead working sessions, labs, and workshops to educate, energize, and mobilize teams
- Articulate how connected AI systems transform productivity, decision‑making, and institutional memory
- Translate emerging AI capabilities into practical, human‑centered solutions that deliver measurable outcomes
Human Centered AI Experience Design
- Define intuitive, trustworthy interaction models for AI‑powered tools across Data \& Analytics
- Identify high‑impact use cases; prioritize ruthlessly and prototype quickly to prove value
- Balance automation with transparency, explainability, and user control to build trust
- Conduct deep user discovery and usability testing to surface real friction points
- Design and refine end‑to‑end human–AI journeys, including feedback and learning loops
- Establish and evangelize clear design principles for responsible, human‑first AI
- Build and lead a small, high‑impact team of engineers and data specialists delivering internal AI knowledge infrastructure
- Address cultural barriers preventing adoption e.g., “I am faster doing it myself” mindset or simply workflow inertia
Cross Functional Partnership \& Scaling Impact
- Partner across product, engineering, legal, compliance, and business teams to ensure responsible rollout
- Implement feedback loops and metrics to track adoption, trust, and productivity impact
- Navigate data governance, privacy, and security requirements in a regulated environment without stalling progress
- Act as an internal thought leader, expanding what leaders believe is possible while grounding expectations in reality
- Codify patterns, playbooks, and standards that can extend beyond Data \& Analytics to the broader enterprise
Skills \& Attributes Critical for Success
- Deeply conversant in AI and large language model capabilities and limitations; able to separate hype from value
- A builder and change leader who modernizes how organizations work, not just by shipping tools, but by reshaping operating models
- Comfortable leading in ambiguity, influencing without authority, and operating in large, complex enterprises
- A compelling communicator who can earn executive sponsorship and bring skeptical stakeholders along
- Biased toward action: you ship early, learn fast, and iterate continuously
- Experienced in consulting, product strategy, platform, or process/people change leadership roles where adoption mattered as much as delivery
- 10\+ years in technology, data, AI, product, or change leadership roles
- Proven track record leading cross‑functional initiatives in large enterprises; regulated industry experience preferred
- Experience building or deploying AI products, knowledge platforms, or enterprise productivity tools
- Strong grounding in user research, prototyping, and iterative delivery
- Demonstrated ability to lead diverse technical teams and drive human adoption of complex systems
- Deep practitioner\-level fluency with LLMs and AI tools – you should be a power user yourself
- Working knowledge of AI/ML concepts, design thinking, and change management; familiarity with AI ethics
- Intellectual curiosity and business judgment prioritized over specific degrees
- Bachelor’s or advanced degree in Human Computer Interaction, Cognitive Science, UX, AI, or related field preferred
- Portfolio, writing, or examples that show how you think about human–AI collaboration
*Important: You do not need to have held a role titled “Human–AI” before.* If you have led technology‑driven change, built products or platforms, redesigned workflows, or driven enterprise adoption, you are strongly in scope.
Why This Role Matters
Insurance runs on knowledge (judgment, experience, interpretation, and history). Today, much of that knowledge is fragmented across people, emails, and documents. AI can unlock it, but only if it is designed for humans. *This role is the tip of the spear:* proving that AI‑native knowledge infrastructure works by deploying it first with the teams building AI at Chubb. If we can’t make it valuable, trusted, and intuitive for ourselves in Data \& Analytics, we should not expect the enterprise to adopt it. You will help define the tools and experiences that drive how work evolves at Chubb, an exciting “pace car of the possible”.
Chubb is a world leader in insurance. With operations in 54 countries, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.
At Chubb, we are committed to equal employment opportunity and compliance with all laws and regulations pertaining to it. Our policy is to provide employment, training, compensation, promotion, and other conditions or opportunities of employment, without regard to race, color, religious creed, sex, gender, gender identity, gender expression, sexual orientation, marital status, national origin, ancestry, mental and physical disability, medical condition, genetic information, military and veteran status, age, and pregnancy or any other characteristic protected by law. Performance and qualifications are the only basis upon which we hire, assign, promote, compensate, develop and retain employees. Chubb prohibits all unlawful discrimination, harassment and retaliation against any individual who reports discrimination or harassment.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Chubb Insurance, 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 $166,983 based on 13,781 positions with disclosed compensation.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Chubb Insurance AI Hiring
Chubb Insurance has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Philadelphia, PA, US, Overland Park, KS, US, Pittsburgh, PA, US. Compensation range: $145K - $276K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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