Interested in this AI/ML Engineer role at Liberty Mutual Insurance?
Apply Now →About This Role
Description
As AI capabilities accelerate, so does the opportunity to fundamentally improve how we work. AI adoption is a key strategic focus for Liberty Mutual, and the Property \& Specialty Product Delivery team is looking for a driven, curious and highly capable professional to help us seize that opportunity.
We are seeking a Director (individual contributor) to own and drive our AI roadmap across Delivery teams, identifying, testing and scaling AI\-enabled solutions that improve the efficiency, quality and breadth of all our rating program launch and maintenance processes. The ideal candidate will bridge the gap between technology and business, translating complex organizational processes into actionable AI applications, and executing swiftly to deliver results. This role requires strong project management skills, a genuine curiosity for AI tools, and the ability to orchestrate across a variety of workstreams, bringing together the work of others toward a cohesive vision, working cross\-functionally with teams including US Data Science and Technology.
Key responsibilities:
- Own and drive the AI roadmap for Delivery, identifying high\-impact opportunities to augment existing processes and sequencing initiatives for maximum value
- Orchestrate a broad set of workstreams, bringing together the work of others and driving each initiative toward a cohesive, high\-quality outcome. Key examples include:
- Augment existing requirements gathering and documentation processes through AI, including support for Tech story build and code generation
- Explore, test and introduce AI applications to enhance pricing tool workflows, including calibration and dislocation analysis
- Identify and introduce AI\-enabled tooling and processes to accelerate filing support and state\-by\-state rollout while improving consistency across states
- Leverage AI tooling to monitor and evaluate rating program performance post\-launch, surfacing insights that drive continuous improvement
- Develop scalable approaches for quick, high\-quality adjustments to rating programs through AI\-augmented processes
- Partner with US Data Science and Technology to ensure AI solutions are technically sound, scalable and aligned to enterprise capabilities
- Proactively surface risks, decisions and dependencies, escalating where appropriate
- Track progress against the AI roadmap, communicate updates to leadership and stakeholders, and iterate based on learnings
Ideal candidate:
The ideal candidate will have:
- A strong foundation in project or program management, with a track record of driving complex, cross\-functional initiatives from idea through to execution
- Demonstrated curiosity about and experience with AI tools, with the ability to quickly assess their practical applicability to business problems
- The ability to bridge the gap between technology and business, translating complex processes into clear AI use cases and working fluently with both technical and non\-technical stakeholders
- A systems mindset: comfort identifying how separate processes and workstreams connect, and a drive to find solutions that improve efficiency and quality holistically
- Strong agency and self\-direction: able to orchestrate the work of others toward a cohesive vision in ambiguous, evolving environments
- Excellent communication and partnership skills, building trust and alignment across teams with varying expertise
- Familiarity with insurance product delivery or rating programs is a plus
- Experience working with Data Science or Technology teams in a business\-facing capacity is strongly preferred
If you're excited by the idea of building something new and leaving a lasting mark on how a world\-class insurance team operates, this is the role for you!
Qualifications* Master's degree or equivalent in mathematics, economics, statistics, or other quantitative field
- Minimum 7 years relevant work experience, typically 10 years
- PhD or equivalent research experience beneficial
- Expert skills in Excel, PowerPoint, and statistical software packages (eg, SAS, Emblem)
- Must have exceptional planning, analytical, decision\-making, communication, and project management skills
- Experience managing or leading others beneficial
About UsPay Philosophy: The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role.
At Liberty Mutual, our goal is to create a workplace where everyone feels valued, supported, and can thrive. We build an environment that welcomes a wide range of perspectives and experiences, with inclusion embedded in every aspect of our culture and reflected in everyday interactions. This comes to life through comprehensive benefits, workplace flexibility, professional development opportunities, and a host of opportunities provided through our Employee Resource Groups. Each employee plays a role in creating our inclusive culture, which supports every individual to do their best work. Together, we cultivate a community where everyone can make a meaningful impact for our business, our customers, and the communities we serve.
We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well\-being. To learn more about our benefit offerings please visit: https://www.libertymutualgroup.com/about\-lm/careers/benefits
Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran's status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.
Fair Chance Notices
- California
- Los Angeles Incorporated
- Los Angeles Unincorporated
- Philadelphia
- San Francisco
Salary Context
This $120K-$225K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Liberty Mutual Insurance, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($172K) sits 5% below the category median. Disclosed range: $120K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Liberty Mutual Insurance AI Hiring
Liberty Mutual Insurance has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Remote, US. Compensation range: $225K - $322K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.