Senior ML Engineer, Responsible AI and Safety

What you'll do

• Lead the end-to-end responsible AI lifecycle by assessing risks and defining safety policies.
• Develop and implement architecture mitigation and safety alignment strategies for generative models.
• Build models, tools, and datasets to monitor and diagnose failures in production systems.
• Design and support human and automated evaluation methods for complex, multi-labeled data.
• Incorporate human and automated feedback to continuously improve feature safety post-launch.
• Conduct failure analysis and robustness testing for machine learning-driven systems.
• Contribute to research on fairness, uncertainty estimation, and model interpretability.

What we're looking for

• 3+ years of proven ability in machine learning, including work with generative models (Transformers, LLMs, VLMs), NLP, or Computer Vision.
• Proficiency in Python and data science libraries with strong skills in data analysis, visualization, and applied ML workflows.
• Excellent interpersonal skills and the ability to translate technical insights for cross-functional partners and senior leadership.
• Experience designing and supporting human and automated evaluations for complex, nuanced, or multi-labeled data.
• Hands-on experience collecting and analyzing language, vision, or multimodal datasets.
• Background in failure analysis, quality engineering, or robustness testing for ML-driven systems.
• Must be comfortable working with sensitive or potentially offensive content.
• BS, MS, or PhD in Computer Science, Machine Learning, or related field, or equivalent experience (preferred).