Senior Applied Scientist, Generative AI
Liberty Mutual · Boston, MA, US
About this role
## Senior Applied Scientist, Generative AI ### About the role GenAI Research and Solution (part of deep learning research) builds generative AI capabilities that directly power Liberty Mutual products. You’ll be an individual contributor providing technical leadership to design, build, and deploy GenAI solutions end-to-end—from research prototype to production service. This is a hands-on role focused on: - Fine-tuning and evaluating small language models (SLMs) - Building retrieval and agentic pipelines (e.g., RAG, corrective RAG, agent orchestration) - Embedding Liberty’s data into products with high standards for accuracy, grounding, latency, and cost - Partnering with engineers to operationalize solutions on Kubernetes and other deployment platforms ### Responsibilities - Design, fine-tune, and deploy language model-based solutions from experimentation through production - Fine-tune and distill SLMs for domain-specific insurance tasks while balancing accuracy, latency, and cost - Build and improve GenAI pipelines, including RAG, corrective RAG, and agentic orchestration (tool use, planning loops, memory) - Develop and maintain scalable data/document/embedding pipelines using MLOps/LLMOps best practices (reproducibility, deployment, monitoring) - Design evaluation suites and guardrails (groundedness, accuracy, safety, regression testing) - Containerize and ship solutions on Kubernetes; partner with engineering teams to operationalize in production - Research and prototype new methods for training/adapting/evaluating generative models; share results with the team - Communicate findings via technical presentations, reports, and recommendations to technical and non-technical stakeholders - Participate in cross-functional working groups and contribute to broader data science best practices ### Preferred qualifications - Ph.D. (Statistics/CS/Math/Econ/Actuarial Science or related) + 2+ years, **or** Master’s + 4+ years, **or** Bachelor’s + 6+ years - Demonstrated expertise with transformer-based language models, including fine-tuning (LoRA/QLoRA) and instruction tuning - Hands-on experience building GenAI pipelines (retrieval design, chunking/embedding strategies, vector search, agentic tool use) - Strong ML/statistics foundation, experimental design, and model evaluation metrics for generative outputs - Proficiency in Python and MLOps practices (Git, code review, collaborative workflows like GitHub/GitLab, experiment tracking such as MLflow) - Proficiency in PyTorch and GenAI ecosystem tools (Hugging Face, LangChain, LlamaIndex, LangGraph) - Experience with workflow orchestration (e.g., Airflow, Luigi) - Experience with Docker and CI/CD pipelines - Experience deploying/scaling containerized workloads with Kubernetes (Helm, GPU-backed services) - Understanding of GPU acceleration and inference/training optimization (mixed precision, quantization, KV caching, request batching) - Experience with multimodal models and cross-modal retrieval (vision-language) - Experience with insurance data and/or regulated-industry constraints (responsible AI review, data governance, auditability) ### Location & work schedule - Candidates within **50 miles** of **Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX** follow a **hybrid** schedule (in office **2 days/week**) - Otherwise, the role is **remote** with **occasional travel** ### About Liberty Mutual Liberty Mutual is committed to creating a workplace where everyone feels valued
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