Machine Learning Engineer

Role Overview

GitHub is hiring an experienced Machine Learning Engineer for the Safety and Integrity mission—helping protect GitHub and its users by detecting and preventing fraud, abuse, malware, spam, fake accounts, inauthentic content, and crypto mining.

You’ll design, build, deploy, and measure agentic solutions that leverage large language models (LLMs), identify abuse-related vulnerabilities, and help set standards for responsible AI moderation and trust & safety.

Responsibilities

• Design, build, and deploy agentic solutions using LLMs to detect and prevent fraud, abuse, and security threats at scale (e.g., content classification and multi-step agentic investigation).
• Build production-grade, reliable systems that operate against high-volume event streams.
• Train, deploy, and serve scalable ML systems on cloud platforms (e.g., Azure AI Foundry).
• Evaluate and improve models and agentic solutions using offline evaluations, including tool-use loops and LLM-as-judge, plus performance metrics and feedback from production.
• Identify product vulnerabilities that enable abuse and advise product teams on new features.
• Collaborate cross-functionally with data scientists, software engineers, product managers, and content moderators to integrate solutions into production.
• Document systems and support peers’ technical growth.

Requirements

• 4+ years experience in machine learning (or related field), or a BS plus 2+ years ML experience, or a relevant MS.

Preferred Qualifications

• Strong understanding of large language models and hands-on experience applying them at scale.