Interested in this AI/ML Engineer role at National Life Group?
Apply Now →About This Role
Come join one of America's fastest\-growing insurance companies. Since 1848, National Life Group has aimed to keep our promises, providing families with stability in good times and in bad. Throughout that history, we have provided peace of mind to those families as they plan their futures.
Our mission extends beyond the insurance and annuities policies that we offer. We strive to make the world a better place through our grants from our charitable foundation, paid volunteer time for our employees, environmentally sustainable and healthy workplaces, and events that promote the work of nonprofits in our own backyard.
We foster a collaborative environment with opportunities for growth and encourage our associates to live our values: Do good. Be good. Make good.
*Please note that we do not offer visa sponsorship for this position.*
Position Summary
The AI \& Automation Lead will partner with Operational leadership to prioritize and execute AI and automation\-centered initiatives that enhance operational efficiency, drive revenue growth, strengthen risk management, and improve customer experiences. This position plays a pivotal role in shaping the future of National Life Group through the strategic application of artificial intelligence and automation within Operations. Reporting to the Sr. Director, Digital Experience, the AI Transformation Lead will operate as an individual contributor, working in close collaboration with enterprise leadership responsible for driving AI. The successful candidate will demonstrate the ability to move from business opportunity to concept to deployment with a Minimum Viable Product (MVP) mindset, followed by iterative improvement. This role requires exceptional consultative, analytical, and communication skills to effectively manage cross\-functional collaboration and deliver measurable business outcomes.
Essential Duties and Responsibilities
Execution and Delivery
- Lead end\-to\-end delivery of AI and automation solutions in operations, from concept definition through deployment and refinement.
- Define and manage project scope, business requirements, timelines, and dependencies.
- Anticipate and mitigate risks associated with delivery and implementation.
- Inspire and guide cross\-functional teams toward achieving business and technology milestones.
- Work closely with leadership to support AI adoption as a way of working for all of operations.
Strategy, Roadmap, and Prioritization
- Assist in the definition and maintenance of the AI transformation strategy for Service \& Operations in alignment with enterprise objectives.
- Develop and manage an Operations AI initiative roadmap, balancing innovation, feasibility, and business value realization.
- Apply an MVP approach to validate opportunities and guide scaling decisions within Operations.
Cross\-Functional Alignment and Governance
- Collaborate across business, technology, data, and risk functions to ensure coordinated delivery of initiatives.
- Partner with compliance, legal, and the AI Governance Committee to ensure responsible, transparent, and compliant AI implementations.
- Facilitate alignment across stakeholders by clearly communicating objectives, trade\-offs, and expected outcomes.
Measurement and Value Realization
- Define and track key performance indicators (KPIs) to evaluate the success and business impact of AI and automation initiatives.
- Leverage data\-driven insights to guide ongoing optimization and future prioritization of AI and automation initiatives in the business.
- Ensure continuous improvement of AI applications to maximize business value and operational effectiveness.
Qualifications
- Bachelor's degree in Business or a related field or minimum of 8 years in product management, with a focus on AI, software, digital platforms, or automation solutions.
- Proven record of delivering software or AI\-driven solutions; experience with vendor partnerships and custom\-built solutions preferred.
- Strong analytical, problem\-solving, and quantitative capabilities; statistical knowledge a plus.
- Exceptional interpersonal and communication skills with the ability to convey complex ideas clearly to diverse audiences.
- Demonstrated success in managing cross\-functional projects within complex organizational structures.
- Agile, consultative, and outcome\-oriented, with the ability to balance innovation and disciplined execution.
- Experience with Life and Annuity operations or working within regulated industries preferred
Benefits
- Your benefits start day one and are flexible and customizable to your and your family's specific needs. Check out the BENEFITS of a Career at National Life!
National Life Group® is a trade name of National Life Insurance Company, Montpelier, VT – founded in 1848, Life Insurance Company of the Southwest, Addison, TX – chartered in 1955, and their affiliates. Each company of National Life Group is solely responsible for its own financial condition and contractual obligations. Life Insurance Company of the Southwest is not an authorized insurer in New York and does not conduct insurance business in New York. Equity Services, Inc., Member FINRA/SIPC, is a Broker/Dealer and Registered Investment Adviser affiliate of National Life Insurance Company. All other entities are independent of the companies of National Life Group.
Fortune 1000 status is based on the consolidated financial results of all National Life Group companies.
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National Life Group
1 National Life Dr
Montpelier, VT 05604
Salary Context
This $131K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At National Life 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($161K) sits 12% below the category median. Disclosed range: $131K to $192K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
National Life Group AI Hiring
National Life Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Montpelier, VT, US. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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