Staff Machine Learning Engineer, AI Security

What you'll do

• Select, adapt, fine-tune, and deploy pretrained models and lightweight classifiers for specific security problems.
• Build reproducible training and evaluation pipelines on the internal ML platform to improve performance and reliability.
• Define the technical vision and multi-quarter modeling roadmap in collaboration with cross-functional teams.
• Conduct model evaluations and performance analysis to improve accuracy, adversarial robustness, and operational efficiency.
• Manage training data quality and the production model lifecycle using monitoring and red-team feedback.
• Establish best practices for responsible ML development, including reproducible experiments and privacy-aware data usage.
• Translate recent research in NLP and large language models into measurable improvements for AI security.
• Mentor engineers and lead technical discussions to shape the team's long-term machine learning capabilities.

What we're looking for

• 8+ years of experience developing machine learning models with substantial hands-on training experience and production impact.
• Strong background in Python programming and deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.
• Deep understanding of neural network architectures, 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.
• Demonstrated rigor in experimental design, including ablation studies, adversarial tests, and error analysis.
• Excellent written and verbal communication skills to explain technical concepts to both technical and non-technical partners.
• Experience applying ML to security, trust and safety, fraud, privacy, or related adversarial domains (preferred).
• Experience with adversarial training, model distillation, active learning, or synthetic-data generation (preferred).