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Glean

AI-powered enterprise search across applications

AI Engineer - Quality and Post-Training

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of industry experience in AI or Machine Learning Engineering.
  • Hands-on experience post-training large language models, including adapting open-weight models to specialized tasks.
  • Experience building evaluations for AI assistant and agent systems, including generating evaluation sets, building LLM-as-a-judge verifiers, calibrating offline and online signals, and exercising judgment about whether a metric reflects quality improvements.
  • Strong coding skills in Python and/or Go; TypeScript is a plus.
  • Proficiency in a machine learning framework of choice and a track record of shipping production machine learning systems.

Responsibilities

  • Use evaluation results and production traces to improve the agent harness, model routing, prompts, and retrieval, balancing quality against latency and cost.
  • Post-train models using supervised fine-tuning and preference- or reward-based optimization, including generating training data.
  • Build reinforcement learning environments for simulating enterprise workflows, including relevant evaluation sets, harness support, and large-language-model-based verifiers for iterative improvement and internal and external benchmarking.
  • Improve representative evaluation sets, LLM judges, and agentic verifiers, and calibrate them against human graders so scores can support release decisions and model training.
  • Work with product, platform, and research teams to deliver quality improvements in production.

Desired Qualifications

  • TypeScript coding skills.

About the Company

Glean

AI-powered enterprise search across applications

Glean builds an AI-powered search and knowledge assistant that helps employees find information across all of a company's apps and data. Its core product is an enterprise search tool that understands natural language queries and retrieves relevant results from documents, conversations, tickets, and other sources, plus a chat assistant that can summarize and answer questions using that same information. The system uses deep learning language models that adapt to a company's own terminology and context, improving relevance without manual tuning, and it offers hosting options to align with security and data policies. Compared with competitors, Glean focuses on cross-application search, organization-specific language learning, and a combined search-and-chat experience that delivers direct answers and summaries from internal knowledge sources. Its goal is to help teams work faster by turning scattered information into easily accessible answers, reducing time spent searching.

Company size
1,001-5,000
Company stage
Series F
Total funding
$805M
Headquarters
Palo Alto, CA
Founded
2019

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