Interested in this AI/ML Engineer role at Nationwide Mutual Insurance Company?
Apply Now →About This Role
If you’re passionate about being part of a dynamic organization that enables a Fortune 100 company with nearly $70 billion in annual sales to drive innovation and adopt new technologies that deliver business results, then Nationwide’s Technology team could be the place for you! At Nationwide®, “on your side” goes beyond just words. Our customers are at the center of everything we do and we’re looking for associates who are passionate about delivering extraordinary care. #LI-RR1
Job Description Summary
If you’re passionate about building and operating secure, reliable technology platforms that help Nationwide deliver extraordinary care, then this opportunity may be the place for you. Our technology workforce embraces an agile, collaborative, and inclusive culture to deliver outstanding solutions and results.
As the AVP, Software Engineer – Artificial Intelligence, you’ll provide strategic direction and executive oversight for an enterprise portfolio of shared AI platforms, systems, agent capabilities, model and tool catalogs, evaluation and observability tooling, reusable services, patterns, guardrails, and controls. You’ll own the portfolio lifecycle—from strategy and build-ahead investment through production readiness, adoption, reliable operation, continuous improvement, and retirement—and lead the people, financial, and supplier strategies required to sustain these capabilities. Job Description
Key Responsibilities
- Defines the strategy, operating model, product discipline, and multi-year roadmap for AI Platform Engineering, Core Capabilities and Operations in alignment with enterprise technology, architecture, security, risk, data, and business priorities.
- Translates enterprise demand, technology and AI trends, adoption data, operational performance, cost, and reuse opportunities into prioritized investments with clear service boundaries, adoption paths, and measurable outcomes.
- Leads the proactive development and responsible adoption of reusable AI and agent capabilities, model and tool catalogs, platform services, testing and evaluation frameworks, observability, and cost-management capabilities.
- Ensures platform investments demonstrate credible demand, architectural fit, measurable reuse, strong developer experience, safe self-service, responsible financial stewardship, and appropriate risk and control requirements.
- Maintains responsible AI, model governance, evaluation, safety, privacy, security, provenance, access, and lifecycle practices for shared AI and agent capabilities.
- Holds accountability for availability, reliability, resiliency, performance, capacity, security, observability, supportability, lifecycle health, and cost management across the assigned AI systems and tooling portfolio.
- Establishes production-readiness, service-management, resilience, security, privacy, access, versioning, migration, deprecation, and retirement practices; monitors adoption, reuse, reliability, quality, risk, cost, and consumer satisfaction.
- Partners with Build Alongside and Build For organizations and enterprise stakeholders to understand needs, co-design reusable solutions, guide adoption, and incorporate feedback into the portfolio roadmap.
- Provides platforms, reference architectures, guardrails, documentation, training, and enablement that help delivery teams create business value more quickly and safely.
- Owns budgeting, forecasting, workforce planning, capital and expense planning, vendor spend, licensing, investment trade-offs, and unit-cost and consumption measures.
- Governs portfolio intake, prioritization, sequencing, dependencies, risk, capacity, supplier performance, and executive reporting across portfolio health, delivery, adoption, operations, financial performance, and outcomes.
- Leads a multidisciplinary software engineering organization, including workforce planning, hiring, performance management, compensation, career development, succession, training, recognition, and organizational effectiveness.
- Builds an inclusive, high-trust organization with clear accountability across product, engineering, architecture, security, and operations; develops leaders and capabilities in modern engineering, cloud, AI, platform, reliability, and operational practices.
May perform other responsibilities as assigned.
Reporting Relationships: Reports to VP, Innovation and Artificial Intelligence. Leads direct and indirect resources and coordinates internal teams, strategic and consulting partners, and third-party suppliers. Leads technology professionals in a highly matrixed environment.
Typical Skills and Experiences
Education: Undergraduate studies in computer science, software engineering, computer engineering, information systems, business, or a related field preferred. Advanced degree preferred.
License/Certification/Designation: Relevant cloud, software engineering, AI, product management, service management, security, architecture, or program management certifications are helpful but not required unless designated by the business.
Experience: Minimum ten years of progressive technology leadership experience across software engineering, platform engineering, technology product management, cloud, infrastructure, operations, or related disciplines. Demonstrated success leading enterprise-scale platforms or shared services; setting product strategy and adoption models; operating production technology; managing budgets, workforce plans, suppliers, and third-party solutions; leading large, multidisciplinary, matrixed organizations; and embedding security, privacy, risk, compliance, and architecture controls without unnecessarily slowing delivery.
Knowledge, Abilities and Skills: Expert knowledge of enterprise technology strategy, software and platform engineering, cloud, AI and emerging technologies, reliability and service management, resilience, observability, security, privacy, identity, risk, compliance, architecture, reusable patterns, guardrails, and modern delivery practices. Advanced knowledge of technology economics, cost optimization, consumption management, portfolio and supplier management. Proven executive influence, communication, talent development, organizational design, change leadership, strategic thinking, decision-making, innovation, learning agility, ambiguity management, and business acumen.
Other criteria, including leadership skills, competencies and experiences may take precedence.
Staffing exceptions to the above must be approved by the hiring manager's leader and Human Resources Business Partner.
Values: Regularly and consistently demonstrates Nationwide Values.
Job Conditions
Overtime Eligibility: Not Eligible (Exempt).
Working Conditions: Normal office environment or hybrid work options may be available.
ADA: The above statements cover what are generally believed to be principal and essential functions of this job. Specific circumstances may allow or require some people assigned to the job to perform a somewhat different combination of duties.
Benefits
We have an array of benefits to fit your needs, including: medical/dental/vision, life insurance, short and long term disability coverage, paid time off with newly hired associates receiving a minimum of 18 days paid time off each full calendar year pro-rated quarterly based on hire date, nine paid holidays, 8 hours of Lifetime paid time off, 8 hours of Unity Day paid time off, 401(k) with company match, company-paid pension plan, business casual attire, and more.
Nationwide is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive culture where everyone feels challenged, appreciated, respected and engaged. Nationwide prohibits discrimination and harassment and affords equal employment opportunities to employees and applicants without regard to any characteristic (or classification) protected by applicable law.
NOTE TO EMPLOYMENT AGENCIES:
We value the partnerships we have built with our preferred vendors. Nationwide does not accept unsolicited resumes from employment agencies. All resumes submitted by employment agencies directly to any Nationwide employee or hiring manager in any form without a signed Nationwide Client Services Agreement on file and search engagement for that position will be deemed unsolicited in nature. No fee will be paid in the event the candidate is subsequently hired as a result of the referral or through other means.
Nationwide pays on a geographic-specific salary structure and placement within the actual starting salary range for this position will be determined by a number of factors including the skills, education, training, credentials and experience of the candidate; the scope, complexity and location of the role as well as the cost of labor in the market; and other conditions of employment. If a Sales job, Sales Incentives, based on performance goals are possible in addition to this range. Note on Compensation for Part-Time Roles: Please be aware that the salary ranges listed below reflect full-time compensation. Actual compensation may be prorated based on the number of hours worked relative to a full-time schedule. The national salary range for AVP, Software Engineer - Artificial Intelligence : $175,000.00-$326,000.00 The expected starting salary range for AVP, Software Engineer - Artificial Intelligence : $175,000.00 - $291,000.00
- Job Category Technology
- Position Type Full Time
- Posted 2026-10-12T18:14:09.692778+00:00
- Location(s) Ohio - Columbus, Three Nationwide Plaza
- Line of Business
- Entity
- Recruiter
- Hiring Manager
- Experience Level
- Req ID 100809
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Nationwide Mutual Insurance Company, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Nationwide Mutual Insurance Company AI Hiring
Nationwide Mutual Insurance Company has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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