Staff Machine Learning Engineer (AI Security)

• The AI Security team within Reddit’s Security Platform Engineering organization builds security into Reddit’s products, engineering systems, AI platforms, and operational infrastructure so the secure path is the easiest path for both people and agents
• A core part of this work is developing practical, high-quality machine learning systems that detect and prevent risks such as prompt injection, jailbreaks, sensitive data exposure, and unsafe or unauthorized AI behavior
• Building on Reddit’s centralized LLM Guardrails Platform, which provides a shared security and safety boundary across major products, we are expanding ML-powered protections as Reddit’s AI systems and threat landscape evolve
• We are looking for a founding Staff Machine Learning Engineer to lead the development, training, and optimization of models for AI security at Reddit
• This is a strategic and hands-on individual contributor role, combining deep ownership of model architecture, training data, and experimentation with technical leadership across teams
• You will help Reddit deliver stronger AI protections while preserving a high-quality product experience
• Select, adapt, fine-tune, evaluate, and deploy pretrained models and lightweight classifiers for Reddit-specific security problems
• 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
• Conduct model evaluations and performance analysis to improve accuracy and adversarial robustness, and define launch criteria that balance false positives, latency, throughput, reliability, and cost
• Own training-data quality and the production model lifecycle, using monitoring, incident findings, and red-team feedback to guide dataset improvements, retraining, and safe rollout or rollback
• Establish best practices for responsible ML development and deployment, including reproducible experiments, testing, model and data lineage, and privacy-aware data use
• Stay current with research in NLP, large language models, and relevant multimodal techniques, translating promising advances into measurable model improvements
• Mentor engineers and lead technical discussions and reviews, shaping the team’s long-term ML capabilities and AI security direction

Benefits

• Comprehensive health benefits
• Flexible vacation & Reddit global days off
• Family planning funds & 4+ months paid parental leave
• Personal & professional development funds
• Paid volunteer time off
• Workspace & home office benefits
• 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