Job Description

Product Content Engineering is a horizontal function supporting initiatives across Meta. We partner closely with product and technical teams to solve problems by providing model-centered solutions, setting standards of quality, and building the frameworks that ensure AI-powered experiences actually work for people.

We're looking for a Content Engineer who will partner with Meta’s agent tooling team to define model quality standards and target behaviors for Meta’s internal AI assistant - an automated tool driving employee productivity across internal systems, data, and workflows. You'll work at the intersection of evaluation: building the frameworks, rubrics, and pipelines that hold AI outputs to a high standard. You'll assess model behavior and hardness, identify where it falls short, and work cross-functionally with engineering, product, research, and data science teams to make it better.

If you're energized by building better AI experiences through rigorous evaluation, thrive in ambiguity, and bring strong editorial and analytical judgment, we encourage you to apply.

Responsibilities

Define quality standards – how well the model is at taking actions, completing tasks, changing states, asking for permission when needed, etc. and use them to systematically evaluate how AI models are performing for Meta’s internal AI assistant.

• Design golden sets, taxonomies, and guidelines that integrate qualitative and quantitative signals.
• Build repeatable workflows for collecting, annotating, and analyzing AI outputs so evaluations can run efficiently as models evolve.
• Evaluate successive model releases through structured comparison, documenting what improved, what regressed, and what to prioritize next.
• Develop processes to track model quality and performance over time and flag regressions.
• Synthesize evaluation results into structured error patterns and concrete recommendations that engineering and product teams can act on.
• Work cross-functionally with engineering, data science, and product teams to align AI behaviors with real-world user expectations.
• Identify and mitigate operational risks — staffing gaps, timeline conflicts, quality regressions — before they impact delivery.

Qualifications

Bachelor's degree in Computer Science, Data Science, Linguistics, or a related field, or equivalent practical experience

• 5+ years of experience working collaboratively with product, engineering, design, and user research teams
• 2+ years working with generative AI products, AI evaluation, prompt engineering, annotation, and/or labeling and analysis
• Experience designing and implementing evaluation frameworks, annotation guidelines, or quality rubrics for agentic systems
• Demonstrated data analysis skills, with experience exploring data, identifying patterns, and producing actionable insights
• Experience applying critical thinking to lead data-driven analyses that inform product or user experience decisions, and communicating findings to executive leadership
• Proven track record of cross-functional collaboration and delivering results in environments with evolving requirements and competing priorities Experience establishing ground truth for text-based agentic workflows (e.g., multi-step reasoning, tool use, end-to-end task completion), including the annotation guidelines and quality rubrics that make evaluations repeatable
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Familiarity with AI evaluation methods such as human eval, model-as-judge, A/B testing, or red-teaming
• Experience building dashboards, scripts, or workflows that codify evaluation metrics
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience with Python, SQL, or other tools for data analysis and evaluation automation
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)