Machine Learning Engineer

GitHub, Inc.•UNAVAILABLE, Ontario

12d•Remote

About The Position

GitHub is seeking an experienced machine learning engineer to join their team. This role will focus on designing, building, and deploying agentic solutions to detect and prevent fraud, abuse, and security threats on the GitHub platform. The engineer will identify new trends related to safety, fraud, and abuse, build scalable solutions, identify vulnerabilities, and measure the impact of their work. The role involves collaboration with various teams, including Copilot, to ensure the responsible use of AI for moderation and trust and safety purposes. The ideal candidate will have a strong foundation in large language models, software engineering skills, and an understanding of online platform trust and safety issues.

Requirements

4+ years experience in machine learning, or related field OR Bachelor's Degree in Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 2+ years experience in machine learning, or related field OR Master's Degree in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field OR equivalent experience.

Nice To Haves

Strong understanding of large language models — how they work — 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 (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 in responsible AI.
Experience in Safety-by-Design.
Experience with managing user data and privacy.
Solid understanding of machine learning algorithms (supervised and unsupervised learning, anomaly detection, etc.) and their practical implementation.

Responsibilities

Design, build and deploy agentic solutions that leverage large language models to detect and prevent fraud, abuse, and security threats at scale — applying LLMs to problems such as content classification and multi-step agentic investigation.
Build well-engineered, production-grade systems that run reliably against high-volume event streams, making effective use of AI coding assistants to accelerate and improve your work.
Build and operate scalable ML systems on cloud platforms (such as 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 provide consultation to product teams reviewing new features.
Collaborate closely with cross-functional teams including data scientists, software engineers, product managers and content moderators to integrate agentic solutions into production systems.
Document the systems you help build and support the technical growth of your peers.