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About This Role
DescriptionThe Data Tools Team (DT2) is a part of the Liberty Mutual USRM Data Office that is leading the charge to transform our data environment by modernizing our platforms and tools, cloud transitions, data science enablement (AI, ML), and analytics tooling for the next generation (GenBI, PBI); to deliver outstanding user experience for the data community. If you are looking to build platforms of the future, DT2 is the team for you.
As a part of DT2, the Domain Director will be responsible for leading platform experience stewardship for core capabilities of the Data platforms, that include but are not limited to Cloud project tooling, Feature Engineering platforms, data collaboration sandbox for machine learning work, SAS Viya, and Microsoft Power BI for GenBI.
\*\*This position may have in\-office requirements dependent upon candidate location\*\*
This role will be leading a team of talented individuals including solution analysists, platform experience stewards, and product owners, who support key data initiatives by establishing the necessary experience. As a leader of Business Concierge \& GenAI User Enablement Domain Product, you will:
- Serve as an expert voice and opinion leader for Business Concierge \& GenAI User Enablement Domain Product tools and technical data science delivery, particularly representing the business unit (profit center) within enterprise owned platforms.
- Lead cross functionally with influence with or without authority. Influence stakeholders in an Agile delivery routine. Direct accountability of enterprise delivery partners in a matrix structure.
- Use strong problem\-solving skills and innovative mindset to build Data Science and analytic tooling, including the machine learning pipeline tooling.
- Lead vision\-setting for owned products. Establish the strategic roadmap for value\-added features enabling data delivery.
- Liaise effectively between business needs and technology teams. Synthesize and clearly articulate value proposition to leadership and stakeholders.
- Engage squads in planning efforts and achieve optimal productivity.
- Lead with the heart of a servant\-leader. Develop and guide the team members to navigate hurdles (internal and external) with delivery. Coach and Mentor. Retain top talent.
- Underwrite a robust tooling experience for the data community of users. Effectively carry stakeholders along with deep understanding of people, process, and technology.
Along with these, you will partner with leaders on various teams to establish a compelling “Why” for different data products, tools, and services, and expertly negotiate the case for successful product delivery within acceptable timeline.
Additional Responsibilities:
- Overseeing all stages of product creation including design and development
- Monitoring and evaluating product progress at each stage of the process
- Liaise with the product team and end\-users to deliver a robust design\-oriented user experience
- Provide Agile consulting, guidance and participate in Scrum meetings and product sprints reviews
- Ensure exemplary communication with all stakeholders including senior business leaders
- Articulate solutions /recommendations to business users. Present analytical content concisely and effectively to non\-technical audiences and influence non\-analytical business leaders to drive major strategic decisions basis analytical inputs
- Coordinate, prioritize and efficiently allocate team resources to critical initiatives: plan resources proactively, anticipate and actively manage change, set stakeholder expectations as required, identify operational risks and enable the team to drive issues to resolution, balance multiple priorities and minimize surprise escalations.
Ideal candidate will have the following preferred skills
- Understanding of End\-to\-End data pipelines
- 10\-15 years of experience working in data solutions delivery and technical product ownership with Data Product experience
- 8\-10 years of relevant industry experience in Data Science and tooling analytics strongly preferred
- BA/BS in Computer Science or closely related fields such as Mathematics, Engineering, Economics, or other quantitative disciplines
- Advanced degree – MSc., PhD., or MBA is a plus
- Strong written and verbal communication skills
- In depth knowledge of ML Ops, AI Ops, and data pipelines
- Experience in Power BI and associated advanced tooling experiences such as Copilot and Fabric
- Knowledge of Data Science Life Cycle along with MLOps functions
- Experience with SAS and SAS Viya
- Knowledge of Self\-Service Analytics concepts such as integrations, access, governance, and orchestration
- Knowledge of CI/CD pipelines and concepts with tools such as GitHub
- Knowledge of GenAI and GenBI concepts, semantic layer models, and related tooling such as MCP servers
- Experience with at least one of the Cloud technologies: AWS, Azure, Snowflake or GCP
- Experience with at least one of the programming languages: Java, Python and Scala
- Prioritization techniques
- Strong analytical and problem solving skills
- Experience in communicating recommendations to senior business leaders highly preferred.
Qualifications* Strong written and oral communication skills required
- Experience in communicating recommendations to senior business leaders preferred
- BA/BS, or relevant work experience, in Computer Science or related field preferred
- MBA or advanced degree in Mathematics, Computer Science, Engineering, Economics, or other quantitative discipline strongly preferred
- 10\+ years of experience working in coding and data solutions design principles, particularly in open source tools and technology, negotiating horizontally and vertically, with a track record of delivering products from ideation to launch
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://LMI.co/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 $137K-$257K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($197K) sits 18% above the category median. Disclosed range: $137K to $257K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Liberty Mutual Insurance AI Hiring
Liberty Mutual Insurance has 16 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Remote, US, Seattle, WA, US, New York, NY, US. Compensation range: $122K - $257K.
Location Context
AI roles in Boston pay a median of $218,900 across 268 tracked positions. That's 19% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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