Staff Applied Researcher, AI Quality – United States Remote | Visa + Salary Insights

📍 Location: us

🏷 Type: Not specified

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Job Overview

This role at GitHub targets a highly experienced Staff Applied Researcher focused on AI Quality, Large Language Models (LLMs), and agentic systems. You will shape evaluation systems powering GitHub Copilot used by millions of developers globally. The ideal candidate is a Senior-Level research engineer with strong production experience in ML evaluation, experimentation, and scalable AI systems. This position blends applied research, engineering execution, and cross-functional leadership to improve AI reliability, safety, and reasoning quality across developer tools.

🌍 Work Eligibility & Location

This is a Remote (US-based) position, meaning candidates must already be authorized to work in the United States. While GitHub is globally distributed, this role focuses on alignment with US engineering and product teams, especially for Copilot development cycles and evaluation pipelines.

💰 Salary Intelligence

This compensation band places the role in a top-tier AI research category. The upper range reflects deep expertise in LLM evaluation systems, production ML pipelines, and high-impact research leadership. Total compensation may include bonuses and equity, making it highly competitive within AI infrastructure and developer tools markets.

📊 Role Breakdown

This position focuses on building next-generation AI evaluation frameworks that directly influence GitHub Copilot and AI-assisted coding tools. You will design systems for code generation evaluation, reasoning benchmarks, and agentic workflow assessment. A major responsibility is building scalable metrics including LLM-judge systems, reward models, and human-in-the-loop pipelines that ensure model reliability at scale. You will also define repeatable methodologies that drive product decisions across AI systems used by millions of developers.

A significant portion of the work involves engineering high-performance pipelines using Python and TypeScript, along with large-scale dataset management and experimentation frameworks. Expect to contribute to benchmark creation (20–30%), evaluation system design (40–50%), and cross-functional collaboration (20–30%). The role demands both research intuition and production engineering discipline, ensuring models are not just better in theory but measurably better in real-world developer environments.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a highly selective role requiring deep expertise in both research and production systems. Candidates are expected to operate at Staff-level scope, influencing architecture and evaluation strategy across GitHub’s AI ecosystem.

🚀 Career Impact

This role delivers significant career acceleration in AI research and systems engineering. You will gain deep exposure to production-grade LLM evaluation frameworks and influence AI used at global scale.

📋 Key Responsibilities

You will design and maintain AI evaluation frameworks that assess code generation quality, reasoning ability, and safety alignment. This includes building LLM judge systems, reward models, and benchmarking pipelines. You will develop scalable tools using Python and TypeScript to automate model evaluation and feedback loops. A key responsibility is improving GitHub Copilot’s performance by identifying weaknesses in model behavior and creating measurable improvements.

You will collaborate with engineers, product managers, and designers to translate research insights into production systems. Additionally, you will lead benchmark creation for complex coding agent tasks and define standards for AI quality measurement across GitHub’s platform.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This is a high-priority Senior-Level AI role with strong market demand. Due to its focus on LLM evaluation systems and GitHub Copilot infrastructure, competition is extremely high. Candidates with production AI experience and benchmarking systems have a clear advantage. Apply early as roles at this level often close quickly once strong candidates are identified.

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✅ Job Source & Verification

This listing is sourced from the official GitHub Careers platform, ensuring high source credibility and accurate role specifications. Information reflects the most recent publicly available job description as of latest posting update. Always verify final details on GitHub’s official careers page before applying.