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Company Details: What makes Admiral Insurance Group *ADMIRABLE*.
Since 1974, Admiral Insurance Group has been supporting business innovation and market growth through our wholesale\-dedicated excess and surplus (E\&S) lines of commercial insurance. We specialize in underwriting difficult\-to\-place moderate to high\-risk commercial businesses that require creative solutions, outside of the box thinking, entrepreneurial spirit and astute business knowledge. As a member of the W. R. Berkley Corporation, a Fortune 500® Company and one of the nation’s premier commercial lines property casualty insurance providers, we have the resources, support and industry data to provide exceptional service and exciting solutions for our clients and partners.
Unlock your *insure*\-ability. Learn more about Careers at Admiral Insurance Group.
See what it’s like to work in Admiral’s Claims department.
The Company is an equal employment opportunity employer.
Responsibilities:
The Chief Claims Officer (CCO) is a critical member of the Senior Leadership Team, accountable for developing and executing the enterprise\-wide claims strategy and driving exceptional performance across claims operations, customer service, compliance, litigation management, and overall loss cost leadership. This executive provides forward\-looking strategic guidance, ensures disciplined operational execution, and builds a high\-performing claims organization that advances the company’s financial strength, reputation, and long‑term growth. Central to the position is an Executive who will embrace and lead through a culture of collaboration and creativity. Strategic Leadership \& Enterprise Alignment* Lead the development, refinement, and execution of a comprehensive claims strategy aligned with enterprise objectives, partnering closely with the President and various departments leaders including Underwriting, Actuary, and Finance.
- Evaluate existing strategies and operating models to identify opportunities for modernization, operational efficiency, and improved claim outcomes.
- Serve as an advisor to the Senior Leadership Team on emerging loss trends, regulatory developments, market shifts, and strategic implications for the business.
Operational Excellence \& Claims Performance* Ensure timely, fair, and consistent claims handling through disciplined adherence to internal claims best practices.
- Oversee the establishment and maintenance of appropriate and timely claim reserves, reinforcing the importance of reserve accuracy in supporting rate adequacy and financial performance.
- Lead the development and continuous enhancement of claims programs, performance metrics, operational frameworks, and compliance controls.
- Champion the effective use of predictive analytics, technology, and automation to advance decision quality, improve operational efficiency, and reduce loss costs.
- Drive cost containment strategies—including both ALAE and ULAE—in partnership with Finance and other business leaders.
Cross\-Functional Collaboration \& Knowledge Sharing* Collaborate across Underwriting, Actuarial, Finance, and other critical functions to ensure alignment on market intelligence, legal changes, loss trends, and strategic assumptions.
- Provide clear, consistent communication to brokers, reinsurers, and other stakeholders regarding claims performance, key developments, and major losses.
Litigation, Legal Strategy \& External Partnerships* Oversee sound litigation management practices including panel counsel selection, performance monitoring, and adherence to established metrics.
- Oversee the detailed review of legal bills using appropriate tools and methodologies to ensure cost optimization and quality representation.
- Participate in mediations, arbitrations, depositions, and trials where executive\-level presence or decision\-making is required.
Organizational Leadership, Culture \& Talent Development* Provide vision, direction, and executive leadership to the Claims organization, cultivating a culture of accountability, communication, collaboration, and continuous improvement.
- Lead all people management activities including hiring, workforce planning, coaching, performance management, and succession development.
- Build talent bench strength by upskilling existing team members and creating a pipeline of strong claims professionals who can support long\-term business needs.
- Set clear expectations, provide frequent and constructive feedback, and create conditions that motivate the team to achieve peak performance.
- Create the Claims annual budget, operational objectives, and performance goals.
Governance, Compliance \& Risk Mitigation* Ensure full regulatory compliance and immediately report all regulatory inquiries while coordinating appropriate responses.
- Maintain a disciplined approach to audits, operational reviews, and internal controls to validate effectiveness and consistency across the claims function.
- Perform other strategic duties or enterprise assignments as requested by the President.
Qualifications:
- Bachelor’s degree required; Juris Doctor strongly preferred; advanced degree a plus
- 15\+ years of claims leadership experience with progressive responsibility.
- Expertise across casualty lines including General Liability, Product Liability, Supported and Unsupported Excess, Professional Liability, and Construction Defect.
- Proven background in large loss management, mediation strategy, and complex settlement negotiations.
- Demonstrated financial acumen in budgeting, loss cost analysis, and evaluating cost–benefit outcomes for initiatives and projects.
Leadership Competencies Leadership Excellence* Sets clear expectations and inspires teams to deliver exceptional results.
- Provides direct, timely feedback and actively seeks input from colleagues and team members.
- Delegates effectively while maintaining accountability and operational oversight.
Communication \& Influence* Demonstrates strong interpersonal, presentation, negotiation, and consultative skills.
- Communicates effectively across all levels of the organization and with external partners.
- Responds promptly to customer needs and maintains a highly collaborative approach with the Leadership Team.
Operational \& Work Management Skills* Maintains a high degree of initiative, professionalism, and positive energy.
- Demonstrates strong organizational and prioritization skills with a sense of urgency toward goals.
- Develops and implements effective audit processes to ensure consistency and high\-quality claims performance.
Analytical \& Strategic Thinking* Identifies patterns and underlying drivers within complex data sets, turning insights into actionable strategies.
- Executes strategic priorities while managing complex claims, negotiating outcomes, and overseeing team activities.
- Continuously identifies opportunities to enhance claims operations, workflows, systems integration, and organizational efficiency.
*\#LI\-FL1 \#LI\-HYBRID*
Additional Company Details: We do not accept any unsolicited resumes from external recruiting firms. The company offers a competitive compensation plan and robust benefits package for full time regular employees which for this role includes: Base Salary Range: $250,000 \- $275,000 The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment. Eligible to participate in the annual discretionary bonus program. Benefits: Health, Dental, Vision, Life, Disability, Wellness, Paid Time Off, 401(k) and Profit\-Sharing plans.
Salary Context
This $250K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Berkley, 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 $166,983 based on 13,781 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $188,800. This role's midpoint ($262K) sits 57% above the category median. Disclosed range: $250K to $275K.
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.
Berkley AI Hiring
Berkley has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Greenwich, CT, US, Irving, TX, US, Jersey City, NJ, US. Compensation range: $160K - $275K.
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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