Machine Learning Safety: Evaluation Research Engineer

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Not verified last checked Jun 13, 2026

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Machine Learning Safety: Evaluation Research Engineer San Francisco, United States of America This role supports the design and development of safety evaluation methodologies for generative and agentic AI features that enable users across the globe to interact with our media products and services. You will play an impactful role: shaping responsible AI and safety policies, evaluating fidelity to product safety requirements, creating risk assessments and taxonomies, curating exemplar safety evaluation datasets, and ensuring that evaluation frameworks are culturally and linguistically grounded. An ideal candidate possesses a strong understanding of issues in responsible AI and A and society, technology evaluation design principles and practices, and brings experience designing evaluations to support policies and/or product requirements, classification systems, and annotation and/or study participant guidelines. Taxonomy Development: Design, refine, and maintain safety-relevant taxonomies that capture risk categories, content types, and policy distinctions, achieved through collaborations with subject matter experts who bring knowledge across languages and cultural contexts. You will work collaboratively to ensure taxonomies are comprehensive, internally consistent, and actionable for downstream evaluation work. Policy-to-Data Translation: Develop and validate exemplar sets that illustrate taxonomy categories, edge cases, and boundary conditions. Collaborate with language and cultural experts to ensure exemplars are culturally appropriate and representative across target markets. Partner with policy, product, and engineering teams to translate responsible AI policies and guidelines into concrete data requirements, annotation schemas, and evaluation criteria that can be operationalized across markets. Develop and maintain synthetic data generation pipelines to augment evaluation coverage, stress-test safety boundaries, and support evaluation

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