AIML - Senior ML Engineer, Responsible AI and Safety

Our team leads Responsible AI initiatives for global generative AI products, operating at the intersection of policy, product, and GenAI. We're seeking candidates who will shape safety policies in partnership with leadership, design, engineering, legal, and regulatory stakeholders—ensuring our safeguards advance both user protection and product innovation. These individuals will work on architecture mitigation and safety alignment strategies for generative models, drive integration in production. Additionally, they will work on developing models, tools, datasets, and evaluation methods to monitor, diagnose failures, and improve the safety of generative models throughout the deployment lifecycle. We do all these by incorporating human and automated feedback, post‑launch to continuously improve feature safety and user trust. Minimum Qualifications 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 (e.g. Pandas) with strong skills in data analysis, visualization, and applied ML workflows Excellent interpersonal skills and proven ability to translate sophisticated technical insights for cross‑functional partners, senior leadership, and executives Strong analytical and independent problem-solving skills, with ability to navigate ambiguity 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 Must be comfortable working with sensitive or potentially offensive content Preferred Qualifications BS, MS, or PhD in Computer Science, Machine Learning, or related field, or equivalent experience Proven success contributing in a highly cross‑functional environment Experience shipping complex AI systems at global scale Background in model explainability, uncertainty estimation, or interpretability Curiosity and research interest in fairness, bias, and the societal impacts of generative AI Passion for building innovative, high‑impact products that draw upon interdisciplinary skills