Description

Develop datasets and models for content safety, production security, robustness, and ML fairness. Research and implement bias detection techniques for LLMs and RAG systems. Define metrics for responsible AI behavior and maintain MLOps standards for automation, monitoring, and scale.

This role is hybrid.

Responsibilities

• Build and train models for content safety and ML fairness evaluation.
• Implement bias detection and mitigation strategies for LLMs and multi-modal systems.
• Track and define key metrics for responsible model behavior and usage.
• Contribute to the MLOps platform and develop safety tools for ML teams.
• Collaborate with engineers and researchers to solve safety and fairness challenges.

Required Skills

• Master’s or PhD in Computer Science, Electrical Engineering, or equivalent experience.
• Minimum 2+ years of experience deploying machine learning models in production.
• Strong proficiency in Python and deep learning frameworks (PyTorch, Keras).
• 1+ year background in content safety, ML fairness, robustness, or model security.
• Experience with large multi-modal datasets and multi-modal models.
• Understanding of LLM alignment, fine-tuning, and VLMs.
• Knowledge of robustness issues including hallucinations and generative misinformation.

Preferred Skills

• Proven experience with multimodal and/or multilingual content safety and regulatory compliance.
• Experience with GenAI security, including prompt stability and adversarial robustness.

Key Skills

Education

Any Graduate