AI Engineer Data and Evals

About Glean:

Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industrys most advanced enterprise search has evolved into a full-scale Work AI ecosystem powering intelligent Search an AI Assistant and scalable AI agents on one secure open platform. With over 100 enterprise SaaS connectors flexible LLM choice and robust APIs Glean gives organizations the infrastructure to govern scale and customize AI across their entire business - without vendor lock-in or costly implementation cycles.

At its core Glean is redefining how enterprises find use and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people content and activity delivering deeply personalized context-aware responses for every employee. This foundation powers Gleans agentic capabilities - AI agents that automate real work across teams by accessing the industrys broadest range of data: enterprise and world structured and unstructured historical and real-time. The result: measurable business impact through faster onboarding hours of productivity gained each week and smarter safer decisions at every level.

Recognized by Fast Company as one of the Worlds Most Innovative Companies (Top 10 2025) by CNBCs Disruptor 50 Bloombergs AI Startups to Watch (2026) Forbes AI 50 and Gartners Tech Innovators in Agentic AI Glean continues to accelerate its global impact. With customers across 50 industries and 1000 employees in more than 25 countries were helping the worlds largest organizations make every employee AI-fluent and turning the superintelligent enterprise from concept into reality.

If youre excited to shape how the world works youll help build systems used daily across Microsoft Teams Zoom ServiceNow Zendesk GitHub and many more. Youll be deeply embedded where people get things done. Youll ship agentic capabilities on an open extensible stack with the craft and care required for enterprise trust as we bring Work AI to every employee in every company.

About the Role:

The bottleneck in building reliable useful AI is no longer writing code. Its defining what good looks like and measuring how well our products deliver it. That takes high-quality data and evals that teams can trust.

Our team owns data and evaluation science for Gleans AI products. We build evalsets agentic LLM-judges and benchmarks that capture what good looks like along with the platform and infrastructure that run evals reliably at scale and enterprise simulation environments where we test how our assistants and agents actually perform. Starting from real production usage youll turn what we learn into evals that guide improvements across the entire stack from agent harnesses to tool use skill iteration model routing and post-training of task specific models.

Youll bring new research into production and partner with R&D product and GTM to turn findings from evals into better products and customer outcomes. If you care about quality and want to build the systems that measure it this role is for you.

You will:

• Build evalsets and benchmarks from expert-curated examples production data synthetic data and past failures covering edge cases and key user slices.
• Build and scale the eval platform and agent observability: experiment pipelines trace capture and replay and dashboards with reproducible affordable runs.
• Build agentic LLM verifiers for correctness groundedness citation quality and task success and calibrate them against human labels using clear rubrics.
• Run evals at every stage from CI and pre-release suites to sampled production traffic and report results with confidence intervals.
• Build enterprise simulation environments with simulated users and realistic company data to test multi-step agent tasks.
• Diagnose failures by slice and trace and turn eval results into concrete fixes weighing quality against latency and cost.

About you:

• 5 years of software engineering experience with strong Go and/or Python and backend fundamentals: APIs data modeling testing and distributed pipelines. TypeScript is a plus.
• Experience building datasets and benchmarks for LLM use-cases RAG or agents including evalset curation and synthetic data generation.
• Familiarity with eval methodologies and their tradeoffs: deterministic metrics LLM-as-a-judge pointwise vs pairwise grading human labeling and judge calibration.
• Experience building eval or experiment infrastructure that runs reliably at scale including CI/CD integrations and observability tooling like OpenTelemetry.
• Strong analytical skills: you dig into traces and failure cases write SQL and use data and metrics to explain why quality moved.
• Nice to have: experience with simulation environments and/or model post-training techniques.

Location:

• This role is hybrid (4 days a week in our Mountain View or San Francisco office)

Compensation & Benefits:

The standard base salary range for this position is $200000 - $280000 annually. Compensation offered will be determined by factors such as location level job-related knowledge skills and experience. Certain roles may be eligible for variable compensation equity and benefits.

We offer a comprehensive benefits package including competitive compensation Medical Vision and Dental coverage generous time-off policy and the opportunity to contribute to your 401k plan to support your long-term goals. When you join youll receive a home office improvement stipend as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events and provide healthy lunches daily to keep you fueled and focused.

Were committed to building and sustaining a diverse inclusive workplace. We strive to attract and retain people with a wide range of backgrounds experiences and perspectives and we do not discriminate on the basis of gender ethnicity sexual orientation religion civil or family status age disability or race.

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AI-First Mindset at Glean:

At Glean AI fluency is core to how we work and were committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process youll complete a brief AI-focused exercise or discussion so we can understand how you think about design and use AI to drive impact in your role. Feel free to reference any tools platforms or workflows you use today prior Glean experience isnt required.

Global Data Privacy Notice for Job Candidates and Applicants:

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By clicking Submit Application I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement and I agree to the terms.

Required Experience:

IC