Senior Machine Learning Engineer, GenAI Security

📋 Description

• Develop and enhance security-oriented ML models for Reddit’s GenAI traffic, which includes guardrail models, semantic classifiers, anomaly detection models, and various neural network-based security signals.

• Take ownership of model development from start to finish: identify security challenges, compile and label datasets, construct ETL pipelines, engineer features, train models, assess quality, deploy to production, monitor performance, and retrain based on production feedback.

• Utilize contemporary deep learning architectures, such as neural networks, transformers, sequence models, embeddings, and model distillation when they are pragmatically applicable.

• Create comprehensive evaluation suites for adversarial examples, challenging negatives, long-context inputs, structured payloads, tool calls, multi-turn workflows, and actual production traffic.

• Enhance model precision, recall, latency, cost, calibration, and operational reliability for high-impact production environments.

• Construct repeatable MLOps workflows for SPACE, which include training pipelines, model lineage, artifact management, holdout evaluation, dashboards, rollback paths, and retraining cycles.

• Collaborate closely with ML Infrastructure, LLM Gateway, DevX, Ads, Answers, Safety, Privacy, Compliance, and other Security teams to integrate security models into real production workflows.

• Work pragmatically with Reddit’s evolving ML platform, leveraging existing infrastructure whenever possible and developing targeted tools as necessary to facilitate ongoing model iteration.

• Convert security objectives into quantifiable model outcomes and assist partners in understanding the trade-offs between risk reduction, latency, false positives, and product impact.

• Offer technical guidance to other engineers and act as a key ML expert for GenAI Security and the broader SPACE model needs.

⛳️ Requirements

• A minimum of 5 years of experience in building, training, evaluating, and deploying production-level ML or deep learning models.

• Practical experience with modern ML frameworks such as PyTorch, TensorFlow, or similar technologies.

• Strong practical knowledge of the complete ML lifecycle: problem definition, data ETL, feature engineering, training, evaluation, deployment, monitoring, debugging, and retraining.

• Experience in creating data pipelines and handling large-scale datasets.

• Proven experience in designing thorough model evaluations, including precision/recall/F1 metrics, false positive analysis, threshold tuning, calibration, holdout sets, regression tests, and production-quality validation.

• Experience in delivering production-quality software, preferably using Python and/or Go.

• Excellent communication skills and the ability to articulate model behavior, risk trade-offs, and technical decisions to cross-functional partners.

• A BS degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.

🏝️ Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs

• 401k with Employer Match

• Global Benefit programs that align with your lifestyle, from workspace to professional development to caregiving support

• Family Planning Support

• Gender-Affirming Care

• Mental Health & Coaching Benefits

• Flexible Vacation & Paid Volunteer Time Off

• Generous Paid Parental Leave