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About This Role
DescriptionWe deliver our customers peace of mind every day by helping them protect what they value most. Our passion for placing the customer at the center of everything we do is driving a transformational shift at Liberty Mutual. Operating as a tech startup within a Fortune 100 company, we are leading a digital disruption that will redefine how people experience insurance.
At Liberty, you'll thrive in a hybrid setting that fosters in-person collaboration, innovation and growth. This approach optimizes both remote and in-person interactions, enabling you to connect and ideate with your team and deepen valuable relationships across the company, while still enjoying the flexibility of remote work for focused tasks and projects.
This role has a hybrid work schedule (2 days onsite) and we are considering candidates based in Portsmouth, NH, Boston, MA, Plano, TX, Indianapolis, IN and Columbus, OH.
You must be authorized to work in the United States without employer sponsorship now or in the future. We will not sponsor employment visas. Candidates who will require future sponsorship, including candidates on CPT/OPT/STEMOPT who will require future sponsorship, are not eligible to apply.
Job Introduction:
Liberty Mutual Technology's Global Digital Services organization is seeking a highly skilled and experienced Technical Program Manager (TPM) to lead program delivery for the AI Backbone, Liberty Mutual's shared enterprise foundation for AI. This role will focus on driving alignment, execution, and adoption across a broad ecosystem of stakeholders as the organization continues to expand and mature AI capabilities across the enterprise.
About the AI Backbone:
The AI Backbone provides the shared capabilities, standards, and reusable services that enable teams across Liberty Mutual to build and deploy AI solutions consistently, securely, and efficiently. Rather than having individual teams create their own approaches to security, governance, observability, identity, and AI operations, the Backbone establishes a common foundation that accelerates delivery while reducing complexity and risk.
Supporting a broad spectrum of AI use cases, from generative AI applications to advanced agentic systems, the Backbone is designed to evolve alongside the rapidly changing AI landscape. It brings together shared technical capabilities, enterprise platforms, governance, and delivery partnerships to accelerate innovation, reduce risk, and enable AI adoption across Liberty Mutual.
The Role of this Technical Program Manager
The TPM joining this program will play a pivotal role in helping Liberty Mutual realize the full potential of AI as an enterprise capability. Working across a diverse ecosystem of technology, data, security, architecture, finance, and business stakeholders, this leader will drive alignment around shared priorities, informed decision-making, and the successful adoption of the capabilities, standards, and operating models that enable AI at scale.
This is a highly visible role with the opportunity to influence how Liberty Mutual scales and governs AI across the enterprise. The successful candidate will bring clarity and momentum to a rapidly evolving landscape, helping leaders make informed decisions while aligning efforts across teams, platforms, and strategic initiatives.
Success in this role requires exceptional program leadership, strong executive presence, and the ability to influence across a highly matrixed organization. The ideal candidate combines strategic thinking with technical curiosity and is comfortable engaging leaders across engineering, security, architecture, data, and business domains to translate enterprise AI strategy into coordinated action and measurable outcomes.
About the job:
This role is responsible for coordinating the planning, governance, and execution required to advance the AI Backbone and enable consistent adoption of shared AI capabilities across Liberty Mutual. The TPM will bring together stakeholders across technology and business domains, helping leaders make informed decisions, manage dependencies, and translate strategic priorities into measurable outcomes.
- Strategic Planning and Execution: Develop and own the multi-year AI Backbone roadmap (Now / Next / Later), sequencing work across shared capabilities, contributing teams, and strategic initiatives. Define target outcomes, phase milestones, and readiness criteria while adapting plans as technologies, priorities, and enterprise needs evolve. Guide the evolution of the Backbone operating model from managed reuse toward federated adoption. Maintain an integrated program plan and delivery rhythm across workstreams, actively managing dependency health, milestone slippage, and re-sequencing as conditions change.
- Enterprise Leadership and Alignment: Lead cross-functional planning, prioritization, and decision forums spanning Global Digital Services (GDS), Global Cyber Security (GCS), Enterprise Data & Data Science (ED&DS), Enterprise Architecture, Enterprise Observability, Finance, and lighthouse delivery teams. Foster shared accountability without direct reporting authority and establish consistent planning, reporting, and governance practices across participating teams. Set the execution quality bar for program and project leads working on AI initiatives across domains, and coach them toward consistent planning, risk, and reporting practices.
- Vendor Management: Coordinate strategic technology partners including AWS, Databricks, Snowflake, Microsoft, Datadog, and Zscaler across evaluation, commitment, and scale-out decisions. Ensure build, buy, and partner strategies align with enterprise priorities, timelines, commercial objectives, and quality expectations.
- Enterprise Adoption and Enablement: Drive adoption of shared AI capabilities, approved patterns, and production-readiness standards across teams with varying priorities, business needs, and risk profiles. Partner across the organization to promote consistent approaches to AI delivery while establishing governance mechanisms that support long-term adoption and scale.
- Risk Management: Proactively identify systemic and cross-program risks spanning identity, governance, model risk, data access, security, and cost. Partner with GCS, ED&DS, LibertyAIR, Enterprise Architecture, and other stakeholders to address risks before they become barriers to adoption or scale. Maintain an enterprise view of risk and dependency health across the AI Backbone portfolio.
- Communication: Serve as the primary author and presenter of AI Backbone updates for Tech ELT, SLT, and business leadership. Translate complex technical concepts, program progress, tradeoffs, and investment decisions into clear, decision-oriented communications and executive-level narratives, including progress-to-goal, decisions required, and risks surfaced ahead of impact.
- Continuous Improvement: Identify opportunities to simplify, automate, and mature the Backbone operating model, reducing manual effort associated with onboarding, readiness assessment, governance, and reporting. Capture lessons learned and emerging best practices and incorporate them into reusable patterns, standards, and operating rhythms.
Additional Responsibilities Specific to this Role
- Portfolio Governance: Design and operate the governance model for the AI Backbone, including decision frameworks, escalation paths, production-readiness criteria, ownership models, and executive scorecards. Shift governance discussions from status reporting to informed decision-making.
- Dependency and Ownership Management: Build and maintain visibility into dependencies across shared capabilities, strategic AI initiatives, and underlying enterprise platforms. Maintain an inventory of AI and agentic initiatives, track maturity and readiness, and help resolve ownership ambiguity across teams and domains.
- Financial and Investment Management: Partner with Technology & Enterprise Finance and Strategy & Transformation to support AI FinOps, investment planning, capacity management, funding requests, and value realization metrics. Provide leaders with the information needed to make informed investment decisions and prioritize resources effectively.
- Technical Judgment and Architecture Engagement: Participate in architecture and design forums to understand tradeoffs across shared capabilities and platforms, and use that understanding to inform sequencing, readiness criteria, and risk assessment.
Scope of Ownership
This position is leveled above the standard Technical Program Manager role based on enterprise scope, stakeholder altitude, and the structural nature of the decisions it drives:
- Enterprise-wide scope. This role oversees a portfolio of shared capabilities, strategic AI initiatives, and the operating model that connects them, spanning multiple technology and business domains.
- Strategic decisions, not execution-level tradeoffs. In partnership with leadership and product teams, this TPM influences priorities, sequencing, investment direction, escalation decisions, and enterprise readiness standards that shape how AI capabilities are delivered and adopted.
- Complex problems with evolving solutions. AI technologies, operating models, and ownership boundaries continue to evolve. The role must bring structure to ambiguity, frame complex cross-organizational challenges, and guide stakeholders toward aligned decisions.
- Manages by exception. Intervenes based on program-level impact rather than task-level detail, letting capability and lighthouse teams own their execution while maintaining visibility into decisions, dependencies, and risks.
- Executive engagement. The role regularly advises and is accountable to Liberty Mutual's senior technology leaders and is responsible for providing clear, actionable insights that inform strategy, prioritization, and investment decisions.
- Impact through influence. Success is measured by alignment across stakeholders, consistency of execution, and the overall health and adoption of the AI Backbone, measured through adoption, readiness, and stakeholder confidence metrics rather than the delivery of any single workstream.
Qualifications* Bachelor's or master's degree in a technical or business discipline, or equivalent experience.
- Minimum 12+ years of technical program management, enterprise transformation, or implementation consulting experience, including 5+ years leading multi-organization, multi-year technology programs in an agile environment.
- Demonstrated ownership of an enterprise platform, shared-services, or foundational capability program (e.g., cloud platform, data platform, identity, developer enablement, or AI/ML platform) where success depended on adoption by teams outside your reporting line.
- Working technical fluency in modern AI and agentic architectures: LLM access and model gateways, agent runtimes and orchestration, memory and semantic layers, evaluation and observability, and identity and governance controls. Familiarity with AWS Bedrock / AgentCore, Databricks / MLflow, Snowflake, Azure Entra ID, Datadog, and MCP / A2A interoperability standards is strongly preferred.
- Proven experience establishing portfolio governance – decision forums, readiness criteria, RACI, escalation frameworks and executive scorecards – across organizations that do not report to you.
- Track record of regular, high-quality communication with C-level and ELT-level technology leadership, including structuring options and investment asks and driving decisions in contentious settings.
- Experience with technology financial management: FinOps, rent / buy / build economics, business cases, capacity planning and value realization tracking.
- Demonstrated leadership of organizational change in large technology organizations, moving teams from local autonomy toward shared standards while preserving delivery velocity.
- Exceptional ability to drive and achieve major transformational outcomes through influence, without direct reporting authority, across engineering, architecture, security, data, finance and business-unit leaders.
- Strong analytical skills and comfort creating and leveraging data (readiness metrics, adoption metrics, dependency and risk density) to drive decision-making.
- Experience coaching or setting standards for other program managers or TPMs; ability to raise the execution bar across a practice.
- Proficiency in developing structured, impactful executive presentations and written narratives.
- Continual knowledge of the evolving AI and technology landscape, internal and external, to shape solutions with technical longevity.
- Bachelor's or master's degree in a technical or business discipline, or equivalent experience.
- In-depth knowledge of IT concepts, strategies and methodologies and their application to business opportunities; in-depth knowledge of agile methodology and the ability to mobilize multiple delivery teams across multiple domains toward a single mission.
- Highly developed negotiation, facilitation and consensus-building skills demonstrated through effective interactions with cross-functional stakeholders.
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 $156K-$281K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $156K to $281K.
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.
Liberty Mutual Insurance AI Hiring
Liberty Mutual Insurance has 3 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Positions span Columbus, OH, Columbus, OH, US. Compensation range: $174K - $281K.
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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