Machine Learning Engineer

GitHub, Inc. is seeking an experienced Machine Learning Engineer to design, build, and deploy agentic LLM-based solutions that help detect and prevent fraud, abuse, and security threats on GitHub at scale. This role also focuses on identifying vulnerabilities that enable abuse, performing ad-hoc analysis, and measuring the real-world impact of safety and integrity efforts.

Responsibilities

• Design, build, and deploy agentic solutions that use large language models to detect and prevent fraud, abuse, and security threats at scale, including applications such as content classification and multi-step agentic investigation.
• Build production-grade systems that run reliably against high-volume event streams, leveraging AI coding assistants to accelerate and improve engineering work.
• Build and operate scalable ML systems on cloud platforms, including Azure AI Foundry, for training, deploying, and serving models and agentic solutions in production.
• Evaluate and improve existing models and agentic solutions using offline evaluations (including tool-use loops and LLM-as-judge evaluation), performance metrics, and feedback from operational deployments.
• Identify vulnerabilities in products that lead to abuse and consult with product teams when reviewing new features.
• Collaborate with cross-functional partners such as data scientists, software engineers, product managers, and content moderators to integrate agentic solutions into production systems.
• Document the systems you build and support the technical growth of peers.

Requirements

• 4+ years experience in machine learning, or a related field.
• OR a Bachelor's Degree in Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or a related field, plus 2+ years experience in machine learning, or a related field.
• OR a Master's Degree in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or a related field.
• OR equivalent experience.
• Strong understanding of large language models, including hands-on experience applying them at scale for classification, agentic workflows, or agents.
• Strong software engineering skills, including experience building with AI coding assistants.
• Experience designing or evaluating agentic systems such as tool-use loops, multi-step workflows, or LLM-as-judge evaluation.
• Hands-on experience building and operating classification or detection systems at scale, including experience with imbalanced data and precision/recall tradeoffs.
• Experience in Trust and Safety, National Security, or fighting spam, malware, fraud, and threat actor activity at scale.
• Experience in responsible AI and Safety-by-Design.
• Experience managing user data and privacy.
• Solid understanding of machine learning algorithms (supervised and unsupervised learning, anomaly detection, etc.) and practical implementation.

Technologies

• Large language models (LLMs)
• Azure AI Foundry
• Tool-use loops
• LLM-as-judge evaluation
• AI coding assistants
• Machine learning
• Precision/recall tradeoffs

What We Value

• Collaboration: the best work is done together.
• Empathy: putting people first.
• Quality: setting the standard for excellence.
• Positive Impact: making the world a better place through the work.
• Shipping: creating things for the people using them.

Compensation

The base salary range for this job is USD $107,700.00 - USD $285,900.00 /Yr.

Locations

• Remote (United States)