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# Applied Scientist, Generative AI

- **Company:** Liberty Mutual Insurance
- **Location:** Boston, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $142,800.0 - $201,300.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Airflow, Code Review, Data Governance, Distributed Computing Environment, Github, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Regression Testing, Search Technologies, QLoRA, Pytorch, LangChain, Retrieval-Augmented Generation, Model Validation, Llamaindex, Gitlab, Git, Discretization, Containerization, Kubernetes, Information Technology, Luigi, HuggingFace, Machine Learning Operations, Mixed Precision, GPT, Software Version Control, Docker
- **Published:** October 8, 2026
- **Apply:** https://careers-libertymutual.icims.com/jobs/262035/senior-applied-scientist%2C-generative-ai/job?mode=apply&apply=yes&in_iframe=1&hashed=-1834448064

## About the Role

* 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., * 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 Master`s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor`s degree(scientific field of study) and a minimum of 5+ years of relevant experience.

## Description

* 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.