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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 Sr Director of AI Engineering to join our Engineering Excellence team in Newport Beach, CA.
As a Sr Director of AI Engineering, you’ll move Pacific Life, and your career, forward by advancing PL engineering through leveraging AI toward agentic adoption where the work supports it or is required.
This role owns delivery across four capability areas for Engineering Excellence (Code \& Delivery, Engineering in Test, Reliability \& Telemetry, Security \& Compliance), ensuring that AI capabilities are operationalized from experimentation into secure, production\-grade systems.
This role operates and drives enterprise adoption of AI\-enabled engineering practices, platform standardization, and governance.
Scope
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Portfolio: Enterprise AI engineering platforms including CI/CD, AI\-assisted development, test generation and quality intelligence, observability/AIOps, and continuous security and compliance
Operating Model: Product based delivery with shared artifacts (Build Register) and continuous feedback loops (Spec Build Apply)
Enterprise Impact: Platforms consumed broadly across engineering to drive productivity, quality, and reliability
Responsibilities
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- Define and execute the enterprise AI Engineering Build roadmap, ensuring priorities are clearly aligned to business outcomes, platform adoption goals, and enterprise engineering needs.
- Build portfolio management: Own the Build Register as the system of record for AI engineering platforms, tools, components, and delivery commitments.
- Planning and prioritization: Operate Spec Build planning loops, using adoption signals and enterprise feedback to shape roadmap priorities.
- AI platform delivery: Lead delivery of production\-ready AI platform capabilities, including LLM integration, RAG systems, and agentic workflows.
- MLOps and LLMOps practices: Establish production\-grade MLOps and LLMOps capabilities, including CI/CD, model lifecycle governance, monitoring, and evaluation.
- Enterprise standards and Responsible AI: Ensure AI engineering platforms meet enterprise expectations for security, privacy, compliance, and Responsible AI.
- Secure\-by\-default engineering: Embed secure\-by\-default practices across AI and engineering workflows, including application security, threat modeling, and policy enforcement.
- Governance and risk partnership: Partner with CISO and risk leadership to define governance expectations, strengthen control alignment, and manage high\-risk releases.
- Cross\-functional alignment: Drive alignment across engineering, product, platform, and security teams to ensure shared priorities, clear decision\-making, and coordinated execution.
- Organization building: Build and scale a high\-performing engineering organization through hiring, rotation planning, leadership development, and capability growth.
- Dependency management: Resolve cross\-team dependencies through lateral alignment, shared accountability, and proactive escalation management.
Required Qualifications
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- Engineering leadership: Proven track record leading multi\-team platform or AI engineering organizations at Director\+ scope.
- Domain depth and breadth: Deep expertise in at least two domains, such as Developer Experience Engineering, Software Engineering in Test, SRE/Observability, or Application Security, with working fluency across the others.
- Production AI delivery: Hands\-on experience building and scaling production AI/ML systems, including LLMs, ML pipelines, or AI platforms.
- Cloud\-native architecture: Strong understanding of cloud\-native architecture such as Azure, AWS, or GCP and single or multi cloud design and deployment
- AI lifecycle governance: Experience implementing MLOps, LLMOps, and AI lifecycle governance practices.
- Security, privacy, and compliance: Strong understanding of secure SDLC, data privacy, and compliance requirements as well as fluency in AI and LLM emerging security challenges.
- Technical risk leadership: Ability to operate as a peer to security leadership and own technical risk conversations.
- Experience level: Minimum 20\+ years of engineering experience, including at least 8 years leading teams at Director\+ scope.
Preferred Qualifications
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- Enterprise AI platform experience: Experience building enterprise AI platforms or scaling enterprise engineering organizations.
- Responsible AI and regulatory fluency: Familiarity with Responsible AI frameworks, such as NIST AI RMF, and related regulatory expectations.
- Agentic architecture exposure: Experience with multi\-agent architectures, orchestration layers, or model routing.
- Regulated industry experience: Experience operating in regulated industries, such as financial services, insurance, or healthcare.
- Platform adoption leadership: Experience managing platform adoption and internal developer experience programs.
Leadership Expectations
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- Strategic systems thinking: Translate AI opportunities into enterprise\-scale platforms that advance engineering productivity, quality, and reliability.
- Innovation with discipline: Balance speed of innovation with governance, security, and operational rigor.
- Team leadership and culture: Build high\-performing teams with strong accountability, engineering excellence, and continuous improvement practices.
- Executive communication and alignment: Communicate effectively with executive stakeholders and drive alignment across enterprise priorities.
- Operational execution: Maintain rigor across delivery planning, prioritization, execution tracking, and outcomes management.
\#LI\-DW1
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.
$203,760\.00 \- $249,040\.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 $203K-$249K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($226K) sits 5% above the category median. Disclosed range: $203K to $249K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Pacific Life AI Hiring
Pacific Life has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Newport Beach, CA, US. Compensation range: $249K - $249K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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