Interested in this AI/ML Engineer role at Everest Re Group?
Apply Now →About This Role
Company:
Everest Reinsurance Company
Job Category:
Technology
Job Description:
About Everest:
Everest is a global leader in risk management, rooted in a rich, 50\+ year heritage of enabling businesses to survive and thrive, and economies to function and flourish. We are underwriters of risk, growth, progress and opportunity. We are a global team focused on disciplined capital allocation and long\-term value creation for all stakeholders, who care deeply about our impact on communities and the wider world.
Role Overview:
Everest is seeking an experienced AI Product Owner to lead the development and delivery of AI\-driven solutions across key business areas, including Underwriting and Claims. This role will bridge business and technology, ensuring that AI use cases are aligned with enterprise strategy and deliver measurable business value. The ideal candidate combines strong business analysis capabilities with technical acumen and deep domain knowledge in insurance operations.
Key Responsibilities:
- Own the end\-to\-end lifecycle of AI products and use cases, from ideation through implementation and scaling.
- Partner with business stakeholders across Underwriting, Claims, and other functions to identify, prioritize, and define high\-impact AI opportunities.
- Translate business needs into clear product requirements, user stories, and acceptance criteria.
- Develop and maintain product roadmaps aligned with enterprise strategic priorities.
- Lead backlog prioritization and sprint planning in collaboration with data science, engineering, and delivery teams.
- Create detailed process maps and identify opportunities for optimization through AI and automation.
- Ensure successful implementation and adoption of AI solutions, including change management and stakeholder communication.
- Monitor product performance, define KPIs, and drive continuous improvement based on insights and feedback.
- Collaborate closely with technical teams to ensure feasibility, scalability, and integration with existing systems.
- Support and enhance enterprise AI platforms (e.g., EverAssist) by aligning use cases and capabilities with business needs.
Required Qualifications \& Skills:
- Bachelor’s degree in Business, Technology, Data Science, or a related field (MBA preferred).
- 5\+ years of experience in Product Ownership, Business Analysis, or related roles.
- Strong domain knowledge of insurance operations, particularly
- Underwriting and Claims.
- Proven experience in implementing AI/ML or advanced analytics use cases in a business environment.
- Excellent business analysis skills, including requirements gathering, process mapping, and documentation.
- Strong understanding of Agile methodologies (Scrum, Kanban) and product management practices.
- Technical acumen with the ability to work closely with data scientists and engineers (familiarity with AI/ML concepts, data pipelines, APIs, etc.).
- Experience with tools such as JIRA, Confluence, and process mapping tools ( e.g., Visio, Lucidchart).
- Strong analytical thinking and problem\-solving skills.
- Excellent communication and stakeholder management skills, with the ability to influence across all levels of the organization.
Preferred Qualifications
- Experience working with enterprise AI platforms or copilots (e.g., EverAssist or similar tools).
- Familiarity with data governance, model risk management, and regulatory considerations in insurance.
- Experience driving digital transformation initiatives in the insurance industry.
The base salary range for this position is $152,000 \- $202,000 annually. The offered rate of compensation will be based on individual education, experience, qualifications and work location. All offers include access to a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, 401k match, retirement savings plan, paid holidays and paid time off (PTO).
\#LI\-Hybrid
\#LI\-AS1
*What if I don’t meet every requirement? At Everest we are dedicated to building an inclusive and authentic workplace. So, if you are excited about this role but your past experience doesn’t align perfectly with every element in the job description, we still encourage you to apply. You may be just the right candidate for this or other roles. Please let us know if you need any accommodations throughout the application or interview process.*
Our Culture
At Everest, our purpose is to provide the world with protection. We help clients and businesses thrive, fuel global economies, and create sustainable value for our colleagues, shareholders and the communities that we serve. We also pride ourselves on having a unique and inclusive culture which is driven by a unified set of values and behaviors.
- Our Values are the guiding principles that inform our decisions, actions and behaviors. They are an expression of our culture and an integral part of how we work: Talent. Thoughtful assumption of risk. Execution. Efficiency. Humility. Leadership. Collaboration. Inclusion and Belonging.
- Our Colleague Behaviors define how we operate and interact with each other no matter our location, level or function: Respect everyone. Pursue better. Lead by example. Own our outcomes. Win together.
All colleagues are held accountable to upholding and supporting our values and behaviors across the company. This includes day to day interactions with fellow colleagues, and the global communities we serve.
Type:
Regular
Time Type:
Full time
Primary Location:
Warren, NJ
Additional Locations:
New York, NY
*Everest is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or creed, sex (including pregnancy), sexual orientation, gender identity or expression, national origin or ancestry, citizenship, genetics, physical or mental disability, age, marital status, civil union status, family or parental status, veteran status, or any other characteristic protected by law. As part of this commitment, Everest will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Everest Benefits at* *[email protected].*
Everest U.S. Privacy Notice \| Everest (everestglobal.com)
Salary Context
This $152K-$202K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1889 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 3,736 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Everest Re Group, 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 $181,357 based on 12,694 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $152K to $202K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,650. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $248,100; VP: $250,000.
Everest Re Group AI Hiring
Everest Re Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Warren, NJ, US. Compensation range: $202K - $220K.
Location Context
Across all AI roles, 15% (562 positions) offer remote work, while 3,158 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,736 open positions tracked in our dataset. By seniority: 109 entry-level, 1,755 mid-level, 1,486 senior, and 386 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (562 positions). The remaining 3,158 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,650. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,736 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,564), Data Scientist (311), AI Software Engineer (277). 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 (109) are outnumbered by mid-level (1,755) and senior (1,486) 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 386 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (562 positions), with 3,158 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 $200,100. Top-quartile roles start at $253,650, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,942 postings), Aws (1,175 postings), Azure (881 postings), Rag (827 postings), Gcp (718 postings), Prompt Engineering (590 postings), Pytorch (586 postings), Claude (528 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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