Staff Machine Learning Engineer, AI Security

Role overview A founding Staff Machine Learning Engineer is needed to lead model development for an AI Security team within a large social platform's Security Platform Engineering organization. The mission centers on building practical, high-quality machine learning systems that detect and prevent risks such as prompt injection, jailbreaks, sensitive data exposure, and unsafe AI behavior across centralized LLM guardrails. This is a strategic, hands-on individual contributor role combining deep model ownership with technical leadership across teams.

Responsibilities - Select, adapt, fine-tune, evaluate, and deploy pretrained models and lightweight classifiers for platform-specific security problems. - Build reproducible training and evaluation pipelines on the 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 and defining architectures across cross-functional teams. - Conduct model evaluations, performance analysis, and adversarial robustness testing, 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 and safe rollouts. - Establish best practices for responsible ML, including reproducible experiments, testing, model and data lineage, and privacy-aware data use, while mentoring engineers and shaping long-term AI security direction.

Requirements - 8+ years of experience developing machine learning models with substantial hands-on training experience and demonstrated production impact. - Strong Python programming, software engineering, and deep learning framework experience (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. - Expertise in scalable data pipelines and distributed training frameworks such as Ray Train or PyTorch Distributed, with understanding of hardware and system tradeoffs. - Demonstrated rigor in experimental design, including representative holdouts, ablation studies, adversarial tests, and error analysis. - Excellent written and verbal communication skills for explaining model behavior, risk, and tradeoffs to diverse audiences.

Nice to have - 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.

Benefits and work setup - Comprehensive healthcare benefits and income replacement programs. - 401(k) with employer match. - Global benefits program covering workspace, professional development, and caregiving support. - Family planning support and gender-affirming care. - Mental health and coaching benefits. - Flexible vacation, paid volunteer time off, and generous paid parental leave. - Remote-eligible role with base salary range of $230,000-$322,000 USD, plus eligibility for equity in the form of restricted stock units and potential commission.