Senior Machine Learning Engineer, GenAI Security

About Our Client: The organization operates a large online platform centered on communities built around shared interests and open conversations. With over 100,000 active communities and approximately 126 million daily unique visitors, it serves as a major source of information on the internet. Within its Security, Privacy, Assurance, and Corporate Engineering division, the GenAI Security team focuses on protecting the use of generative AI across internal tools, employee agents, and user-facing production systems. The team builds zero-trust, defense-in-depth systems that verify identity, permissions, data access, and semantic intent across AI workflows, aiming to secure AI traffic and GenAI adoption by default. About the Opportunity :The Senior Machine Learning Engineer, GenAI Security will lead the development of machine learning models designed to detect and prevent security risks in generative AI workflows. This role involves owning the full ML lifecycle to create reliable, production-ready models that improve security across the platform’s AI traffic. The position has a direct impact on safeguarding AI systems by building innovative defense mechanisms and establishing strong ML practices within the security domain. The engineer will collaborate across multiple teams to integrate models into production environments and guide technical strategy for GenAI security challenges . Responsibilitie s:Build and enhance security-focused ML models for generative AI traffic, including guardrail models, semantic classifiers, and anomaly detectio n.Manage end-to-end model development: problem definition, dataset assembly and labeling, ETL pipeline construction, feature engineering, training, evaluation, deployment, monitoring, and retrainin g.Apply modern deep learning architectures such as neural networks, transformers, and sequence models where appropriat e.Design rigorous evaluation methods addressing adversarial examples, complex inputs, multi-turn workflows, and real production traffi c.Optimize models for precision, recall, latency, cost, calibration, and operational reliabilit y.Develop repeatable MLOps workflows, including training pipelines, artifact management, evaluation, dashboards, rollback, and retraining mechanism s.Collaborate closely with infrastructure, product, privacy, and compliance teams to operationalize security model s.Work pragmatically with evolving ML platforms, balancing existing infrastructure use and new tooling developmen t.Translate security objectives into measurable model outcomes and communicate tradeoffs effectively to stakeholder s.Provide technical leadership and serve as an ML expert for GenAI Security and related model need s. Requiremen ts:Minimum 5 years of experience building, training, evaluating, and deploying production ML or deep learning mode ls.Proficiency with modern ML frameworks such as PyTorch, TensorFlow, or equivalen ts.Deep understanding of the full ML lifecycle including data pipelines, feature engineering, training, evaluation, deployment, and maintenan ce.Experience working with large-scale datasets and building data pipelin es.Expertise in designing rigorous model evaluations including precision, recall, false positive analysis, threshold tuning, calibration, and holdout testi ng.Experience shipping production-quality software, preferably in Python and/or Go.Strong communication skills for explaining model behavior, risks, and technical decisions to cross-functional tea ms.Bachelor’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experien ce.Preferred experience in applying ML to security, privacy, trust and safety, adversarial ML, or generative AI security challeng es.Familiarity with training or fine-tuning neural text models for complex inputs such as long-context prompts, multi-turn interactions, or tool calls is a pl us.Knowledge of production MLOps or model serving systems like Airflow, Ray, MLflow, Triton, ONNX, Kubernetes, or similar is benefici al.Experience improving model quality through labeling strategies, hard-negative mining, synthetic data, distillation, or active learning is advantageo us. Pay Range and Compensation Pack age:This job is eligible for equity in the form of restricted stock units and may include commission based on posit ion.The pay range and compensation package for this role will be determined based on the candidate’s experience, skills, and other relevant fact ors. Benefits & P erks:Comprehensive healthcare and income replacement pro grams401(k) plan with employer matchGlobal bene