Senior Machine Learning Engineer, GenAI Security

About the role

Reddit is a community of communities built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet with 100,000+ active communities and approximately 130 million daily active unique visitors. The GenAI Security team within Reddit's Security, Privacy, Assurance, and Corporate Engineering organization protects Reddit's GenAI usage across employee tools, internal agents, and production user-facing systems to secure and protect Reddit's AI traffic and GenAI adoption by default. We are building zero-trust, defense-in-depth systems that verify identity, permissions, data access, and semantic intent across AI workflows, developing practical high-quality ML models that detect and prevent security risks such as prompt injection, jailbreak attempts, sensitive data exfiltration, unsafe model behavior, anomalous usage, and unauthorized agent actions. This role owns the full machine learning lifecycle including problem definition, data ETL, feature engineering, model training, model evaluation, deployment, experimentation, prediction, monitoring, debugging, and retraining while providing technical direction and serving as a go-to ML expert. The hire will partner closely with ML Infrastructure, LLM Gateway, DevX, Ads, Answers, Safety, Privacy, Compliance, and other Security teams to bring security models into real production workflows.

Key facts

What you'll do

Build and improve security-focused ML models for Reddit's GenAI traffic, including guardrail models, semantic classifiers, anomaly detection models, and other neural network based security signals.

Own model development end to end: define the security problem, assemble and label datasets, build ETL pipelines, engineer features, train models, evaluate quality, deploy to production, monitor performance, and retrain from production feedback.

Use modern deep learning architectures, including neural networks, transformers, sequence models, embeddings, and model distillation where they are the right practical fit.

Design rigorous evaluation suites for adversarial examples, hard negatives, long-context inputs, structured payloads, tool calls, multi-turn workflows, and real production traffic.

Improve model precision, recall, latency, cost, calibration, and operational reliability for high-impact production surfaces.

Build repeatable MLOps workflows for SPACE, including training pipelines, model lineage, artifact management, holdout evaluation, dashboards, rollback paths, and retraining loops.

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

Work pragmatically with Reddit's evolving ML platform, using existing infrastructure where possible and building focused tooling when needed to keep model iteration moving.

Translate security goals into measurable model outcomes and help partners understand tradeoffs between risk reduction, latency, false positives, and product impact.

Provide technical direction to other engineers and serve as a go-to ML expert for GenAI Security and broader SPACE model needs.

Requirements

5+ years of experience building, training, evaluating, and deploying production ML or deep learning models.

Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or similar.

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

Experience building data pipelines and working with large, multi-terabyte scale datasets and high-cardinality categorical features.

Experience deploying models in production and operating them at scale, including managing latency, throughput, availability, and cost.

Experience with experiment tracking, model versioning, and robust evaluation practices across large, complex datasets.

Strong coding skills in Python, including data wrangling, testing, and debugging in collaborative environments.

Strong written and verbal communication skills to translate complex technical concepts to both technical and non-technical stakeholders.

Nice to have

Experience with security, safety, or privacy domains including content moderation, fraud detection, or abuse prevention.

Experience with large language models, including fine-tuning, prompt engineering, and evaluation of generative outputs.

Experience with distributed training, inference optimization, and model compression techniques such as quantization or pruning.

Experience with streaming data pipelines and real-time inference systems.

Experience contributing to open source ML projects or publishing model performance benchmarks.

Practical notes

Engagement: Full-time

Location: Remote

United States

This role is based in the United States and requires availability during regular business hours.