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About This Role
Job Description:
Providing for loved ones, planning rewarding retirements, saving enough for whatever lies ahead – our policyholders count on us to be there when it matters most. It’s a big ask, but it’s one that we have the power to deliver when we work together. We collaborate and innovate – pushing one another to transform not just Pacific Life, but the entire industry for the better. Why? Because it’s the right thing to do. Pacific Life is more than a job, it’s a career with purpose. It’s a career where you have the support, balance, and resources to make a positive impact on the future – including your own. We’re actively seeking a talented AI and Analytics Specialist to join our Workforce Benefits team.
You will work at our state-of-the-art Newport Beach headquarters or Charlotte, NC office.
As an AI and Analytics Specialist, you will move Pacific Life, and your career, forward by designing and implementing AI and analytics solutions that improve operational efficiency, customer experience, decision-making, and business growth. You will join the Workforce Benefits AI & Analytics team and partner across business, technology, data, and risk functions to bring AI opportunities from concept through production deployment and measurable value realization.
How you will help move us forward:
- Lead the design and implementation of AI and advanced analytics solutions that address strategic business opportunities across Workforce Benefits, including underwriting, claims, enrollment, service, sales, and operations.
- Develop and deploy generative AI, machine learning, and advanced analytics capabilities, including knowledge extraction, conversational assistants, retrieval-augmented generation (RAG), agentic workflows, and decision-support solutions.
- Partner with business leaders and technology teams to translate business needs into scalable solutions, integrating AI with enterprise platforms, data sources, and operational workflows.
- Own initiatives from proof of concept through production deployment, adoption, monitoring, governance, and business value realization.
- Support both transformational AI programs and smaller productivity-focused use cases, helping teams identify opportunities, rapidly prototype solutions, develop reusable assets, and scale successful ideas across the division.
- Deliver actionable insights, dashboards, and recommendations that improve decision-making and demonstrate business impact while applying responsible AI practices, including evaluation, privacy, security, monitoring, and risk management.
The experience you bring:
- 3+ years of experience in AI, analytics, data science, machine learning, or related disciplines, including experience delivering business solutions from concept through production.
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related field, or equivalent experience.
- Experience with enterprise AI platforms and large language models such as AWS Bedrock, Snowflake Cortex, Azure, OpenAI, or Google Gemini.
- Hands-on experience with AI application patterns including prompt engineering, RAG, knowledge extraction, evaluation frameworks, AI agents, and workflow automation.
- Strong Python skills and experience with machine learning, statistical analysis, predictive modeling, APIs, cloud platforms, and modern software deployment practices.
- Familiarity with DevOps/MLOps, version control, testing, monitoring, and production support.
- Strong problem-solving, communication, and stakeholder management skills with the ability to explain complex technical concepts to non-technical audiences.
What makes you stand out:
- Experience in Workforce Benefits or Insurance, particularly within underwriting, claims, enrollment, benefits operations, customer service, or sales.
- Experience building production AI solutions while also driving AI adoption, enablement, training, and change management across business teams.
- Experience developing knowledge assistants, copilots, AI agents, automated workflows, or other scalable AI capabilities.
- Familiarity with Microsoft Copilot, Copilot Studio, Power BI, Tableau, Sigma, Salesforce CRM Analytics, or similar enterprise platforms.
- Demonstrated ability to identify AI opportunities, develop business cases, define success metrics, and deliver measurable business outcomes.
You can be who you are.
People come first here. We’re committed to an inclusive workforce. Learn more about how we create a welcoming work environment at www.pacificlife.com. What’s life like at Pacific Life? Visit Instagram.com/lifeatpacificlife.
#LI-KP1
- A product-oriented mindset that balances innovation, governance, usability, scalability, and business value.
#LI-KP1
Base Pay Range:
The base pay range noted represents the company’s good faith minimum and maximum range for this role at the time of posting. The actual compensation offered to a candidate will be dependent upon several factors, including but not limited to experience, qualifications and geographic location. Also, most employees are eligible for additional incentive pay.
$113,490.00 - $138,710.00Your Benefits Start Day 1
Your wellbeing is important to Pacific Life, and we’re committed to providing you with flexible benefits that you can tailor to meet your needs. Whether you are focusing on your physical, financial, emotional, or social wellbeing, we’ve got you covered.
- Prioritization of your health and well-being including Medical, Dental, Vision, and Wellbeing Reimbursement Account that can be used on yourself or your eligible dependents
- Generous paid time off options including: Paid Time Off, Holiday Schedules, and Financial Planning Time Off
- Paid Parental Leave as well as an Adoption Assistance Program
- Competitive 401k savings plan with company match and an additional contribution regardless of participation
You Can Be Who You Are
We are committed to a culture of diversity and inclusion that embraces the authenticity of all employees, partners and communities. We support all employees to thrive and achieve their fullest potential.
What’s life like at Pacific Life? Visit Instagram.com/lifeatpacificlife
EEO Statement:
Pacific Life Insurance Company is an Equal Opportunity /Affirmative Action Employer, M/F/D/V. If you are a qualified individual with a disability or a disabled veteran, you have the right to request an accommodation if you are unable or limited in your ability to use or access our career center as a result of your disability. To request an accommodation, contact a Human Resources Representative at Pacific Life Insurance Company.
Salary Context
This $113K-$138K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Pacific Life, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($126K) sits 41% below the category median. Disclosed range: $113K to $138K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Pacific Life AI Hiring
Pacific Life has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Charlotte, NC, US, Newport Beach, CA. Compensation range: $138K - $138K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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