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 generative AI.
• Design human and automated evaluation methods for complex and multi-labeled data.
• Integrate safety safeguards into production systems throughout the deployment lifecycle.
• Analyze multimodal datasets to improve model robustness, fairness, and uncertainty estimation.
• Incorporate human and automated feedback to continuously improve feature safety post-launch.
• Conduct research on the societal impacts and technical robustness of large-scale AI systems.

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.
• BS, MS, or PhD in Computer Science, Machine Learning, or related field, or equivalent experience (preferred).