Senior Machine Learning Engineer III

Role Description

We are seeking an AI Engineer focused on Retrieval-Augmented Generation (RAG) to help design, build, and optimize AI-powered experiences. In this role, you will contribute to the development of retrieval systems, evaluation frameworks, and source-grounded Generative AI solutions that enable users to access trustworthy and relevant information. You will work with advanced AI technologies while helping ensure quality, accuracy, and responsible AI practices.

Responsibilities

• Design, build, and optimize Retrieval-Augmented Generation (RAG) applications.
• Develop and manage document ingestion, chunking, embedding generation, retrieval, reranking, and source-grounded response workflows.
• Design and operate vector search solutions using modern vector database technologies.
• Validate retrieval quality, citation accuracy, relevance, coverage, and factual grounding using reproducible evaluation datasets.
• Collaborate with cross-functional teams to improve response quality and reduce hallucinations.
• Implement hybrid search strategies combining keyword, metadata, semantic/vector search, and reranking techniques.
• Monitor and evaluate retrieval performance, latency, cost, and AI system quality.
• Support the development of scalable and responsible Generative AI solutions across legal and professional information products.

Qualifications

• Hands-on experience with Retrieval-Augmented Generation (RAG) applications, including document ingestion, chunking, embedding generation, retrieval, reranking, and source-grounded responses.
• Strong understanding of Generative AI and LLM capabilities, limitations, hallucination mitigation, and response-quality evaluation.
• Experience designing and operating vector search solutions using Qdrant, Pinecone, pgvector, LanceDB, OpenSearch vector search, or equivalent technologies.
• Ability to validate retrieval quality, citation accuracy, relevance, coverage, and factual grounding using reproducible evaluation datasets.

Preferred Qualifications

• Experience building AI agents or multi-step workflows with tool use, memory, and human review.
• Familiarity with agentic and LLM frameworks such as Google ADK, LangChain, LiteLLM, or MCP.
• Experience with hybrid search strategies combining keyword, metadata, semantic/vector search, and reranking.
• Experience applying RAG or Generative AI to legal, financial, publishing, or other highly regulated content domains.
• Knowledge of LLM observability, including monitoring latency, cost, retrieval performance, and unsafe or unsupported outputs.

Benefits

• Promote a healthy work/life balance across the organization.
• Numerous wellbeing initiatives.
• Shared parental leave.
• Study assistance and sabbaticals.

Working Pattern

• Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.