Machine Learning Engineer

GitHub is hiring an experienced Machine Learning Engineer to design, build, and deploy agentic LLM-based solutions that help detect and prevent fraud, abuse, and security threats at scale.

Responsibilities

• Design, build, and deploy agentic LLM solutions for fraud, abuse, and security threat detection and prevention, including uses such as content classification and multi-step agentic investigations.
• Develop production-grade systems that run reliably with high-volume event streams, leveraging AI coding assistants to accelerate and improve engineering output.
• Build and operate scalable ML systems on cloud platforms (for example, Azure AI Foundry) to train, deploy, and serve models and agentic solutions in production.
• Evaluate and improve models and agentic systems using offline evaluations, including tool-use loops and LLM-as-judge evaluation, plus performance metrics and feedback from production deployments.
• Identify product vulnerabilities that enable abuse and provide consultation to product teams on reviewing new features.
• Collaborate with cross-functional partners, including 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 and hands-on experience applying them at scale, ideally 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 handling 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 with responsible AI and Safety-by-Design.
• Experience managing user data and privacy.
• Solid understanding of machine learning algorithms (including supervised and unsupervised learning and anomaly detection) 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
• Empathy
• Quality
• Positive Impact
• Shipping

Compensation

• Base salary range: USD 107,700.00 - USD 285,900.00 per year

GitHub Leadership Principles

• GitHub values: Customer-obsessed; Ship to learn; Growth mindset; Own the outcome; Better together; Diverse and inclusive
• Manager fundamentals: Model; Coach; Care
• Leadership principles: Create clarity; Generate energy; Deliver success

Location

• Remote (United States)