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 human and automated evaluation methods for complex and multi-labeled data.
• Analyze multimodal datasets to improve the safety and robustness of large language models.
• Incorporate human and automated feedback to continuously improve feature safety post-launch.
• Conduct research on model fairness, uncertainty estimation, and societal impacts of AI.

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.
• Experience designing and supporting human and automated evaluations, particularly with 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.
• Excellent interpersonal skills to translate technical insights for cross-functional partners, senior leadership, and executives.
• Strong analytical and independent problem-solving skills with the ability to navigate ambiguity.
• 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).
• Proven success contributing in a highly cross-functional environment (preferred).
• Experience shipping complex AI systems at global scale (preferred).
• Background in model explainability, uncertainty estimation, or interpretability (preferred).