Staff Machine Learning Engineer (AI Security)

Join Reddit's AI Security team as a Staff Machine Learning Engineer. In this strategic and hands-on role, you will lead the development, training, and optimization of models for AI security. You will work on building high-quality machine learning systems that detect and prevent risks such as prompt injection, jailbreaks, and sensitive data exposure. You will also set the technical vision and multi-quarter modeling roadmap, conduct model evaluations, and establish best practices for responsible ML development and deployment.

• Lead the development, training, and optimization of machine learning models for AI security at Reddit, focusing on detecting and preventing risks such as prompt injection and sensitive data exposure.
• Build reproducible training and evaluation pipelines on Reddit’s ML platform, partnering with platform engineers to improve inference performance, resource efficiency, and operational reliability.
• Set the technical vision and multi-quarter modeling roadmap, partnering with cross-functional teams to gather requirements, define model architectures, and iterate on model development.

• Expertise in scalable data pipelines and distributed training frameworks such as Ray Train or PyTorch Distributed, with a strong understanding of hardware and system tradeoffs
• Strong background in Python programming, software engineering, and deep learning frameworks and libraries such as TensorFlow, PyTorch, or Hugging Face Transformers
• Deep understanding of neural network architectures and optimization, with proficiency in data preprocessing, tokenization, embeddings, language modeling, and model calibration
• Excellent written and verbal communication, with the ability to explain model behavior, security risk, uncertainty, and tradeoffs to technical and non-technical partners
• 8+ years of experience developing machine learning models, with substantial hands-on model training experience, demonstrated production impact, and a record of leading complex initiatives across teams
• Demonstrated rigor in experimental design and model evaluation, including representative holdouts, ablation studies, adversarial tests, and error analysis to diagnose training issues, bias, and generalization gaps
• Experience applying ML to security, trust and safety, fraud, privacy, or related adversarial domains
• Experience with adversarial training, model distillation, active learning, or synthetic-data generation to improve model quality, robustness, and training efficiency