Staff Machine Learning Engineer, AI Security

Reddit · Remote - United States

About the Role The AI Security team within Reddit’s Security Platform Engineering organization builds security into Reddit’s products, engineering systems, AI platforms, and operational infrastructure. 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. Responsibilities 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, gathering requirements, defining model architectures, and iterating on model development. Conduct model evaluations and performance analysis to improve accuracy and adversarial robustness, defining 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. Requirements 8+ years of experience developing machine learning models with substantial hands‑on model training experience and production impact. Strong background in Python programming, software engineering, and deep learning frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers. Deep understanding of neural network architectures, optimization, data preprocessing, tokenization, embeddings, language modeling, and model calibration. Expertise in scalable data pipelines and distributed training frameworks such as Ray Train or PyTorch Distributed, with a strong understanding of hardware and system trade‑offs. Demonstrated rigor in experimental design and model evaluation, including holdouts, ablation studies, adversarial tests, and error analysis. Excellent written and verbal communication skills. Eligibility No specific degree, course, specialization, or graduation year requirements are mentioned.