Senior Applied Scientist, Generative AI

Senior Applied Scientist, Generative AI

Job Locations US-MA-Boston | US-Remote | US-OH-Columbus | US-WA-Seattle | US-NH-Portsmouth | US-TX-Plano

ID 2026-262035 Position Type Full-Time Minimum Salary USD $120,000.00/Yr. Maximum Salary USD $225,000.00/Yr. Typical Starting Salary $142,800 - $201,300 Flexible Time Off Annual Accrual - days 20 Application Deadline 10/13/2026

Description At GenAI Research and Solution (part of deep learning research team within modeling sophistication, DSE), we research and build generative AI (GenAI) capabilities that go directly into Liberty Mutual products. We fine-tune small language models (SLMs), design retrieval and agentic pipelines, explore creative ways to embed Liberty’s data in products, and hold all these solutions to a high bar for accuracy, grounding, latency and cost. Our team emphasizes technical rigor, reproducibility and methodological innovation, and we work close to the business so that research turns into products our customers use.

As an individual contributor on the team, you will provide technical leadership, design, build and deploy GenAI solutions end to end — from research prototype to production service. You will fine-tune and evaluate small language models, build pipelines such as retrieval-augmented generation (RAG), and variants such as corrective RAG and agentic orchestration, and partner with engineers to run them reliably on our Kubernetes and other deployment platforms. This is a deeply hands-on role with room to shape methodology and influence how GenAI shows up across our products.

Candidates who live within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX will follow a hybrid schedule, coming into the office two days per week. Otherwise, this role is remote with occasional travel.

Responsibilities:

• Design, fine-tune and deploy language model based solutions, from research and experimentation through production implementation.

• Fine-tune and distill small language models for domain-specific insurance tasks, balancing accuracy, latency and cost.

• Build and improve GenAI pipelines, including RAG, corrective RAG and agentic orchestration with tool use, planning loops and memory.

• Develop and maintain scalable data, document and embedding pipelines, applying MLOps and LLMOps best practices for reproducibility, deployment and monitoring.

• Design evaluation suites and guardrails for GenAI use cases, covering groundedness, accuracy, safety and regression testing as models and prompts change.

• Containerize and ship solutions on Kubernetes, partnering with engineering teams to operationalize them in production environments.

• Research and prototype new methods for training, adapting and evaluating generative models, and share what works with the wider team.

• Communicate findings through technical presentations, reports and recommendations to both technical and non-technical stakeholders.

• Participate in cross-functional working groups and contribute to the broader data science community to promote best practices.

Preferred qualifications:

• Ph.D. in Statistics, Computer Science, Mathematics, Economics, Actuarial Science or a related quantitative field with 2+ years of relevant experience; or Master’s with 4+ years; or Bachelor’s with 6+ years.

• Demonstrated expertise with transformer-based language models, including fine-tuning techniques such as parameter-efficient fine-tuning (LoRA/QLoRA) and instruction tuning.

• Hands-on experience building GenAI pipelines, including retrieval design, chunking and embedding strategies, vector search and agentic tool use.

• Strong foundation in machine learning, statistics, experimental design and model evaluation metrics, including the evaluation of generative output.

• Proficiency in Python and MLOps practices, with experience in version control (Git), code review, collaborative development workflows (e.g., GitHub/GitLab) and model versioning/experiment tracking (e.g., MLflow).

• Proficiency in PyTorch and the GenAI ecosystem, such as Hugging Face, LangChain, LlamaIndex or LangGraph.

• Experience building and managing pipelines with workflow orchestration tools (e.g., Airflow, Luigi).

• Ability to clearly communicate technical concepts to diverse audiences.

• Track record of advancing research projects from ideation to implementation.

• Experience with Docker and CI/CD pipelines.

• Experience deploying and scaling containerized workloads with Kubernetes, including Helm and GPU-backed services.

• Understanding of GPU acceleration, distributed training and inference optimization techniques (e.g., mixed precision, quantization, KV caching, request batching).

• Experience with multimodal models and cross-modal retrieval, including vision-language models.

• Experience with insurance data, or with regulated-industry constraints such as responsible AI review, data governance and auditability.

Qualifications

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Expert knowledge of predictive toolset; reflects as expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU.
• Ability to give effective training and presentations to peers, management and less senior business leaders.
• Ability to use results of analysis to persuade team or department management to a particular course of action.
• Has a value driven perspective with regard to understanding of work context and impact.
• Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 2 years of relevant experience, a Masters degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelors degree(scientific field of study) and a minimum of 5+ years of relevant experience.

About Us Pay 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

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