Top AI Ethics Jobs in Los Angeles, CA

As a Senior Associate in Audit and Assurance, you'll evaluate AI solutions, manage AI/ML controls, and enhance transparency in reporting. Responsibilities include data analysis for governance strategies, guiding compliance practices, and contributing to innovative thought leadership in AI governance.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

Lead AI safety strategies for autonomous vehicles, ensuring compliance with safety standards, mentoring teams, and guiding technical projects in AI/ML development for safety-critical applications.

As the Co-Founder & CPTO, you will lead the development of an AI compliance platform, establishing architecture, overseeing product development, and ensuring explainability and reliability along with market representation and capital raising efforts.

The AI Change Senior Manager drives enterprise AI change strategy through coherent activation plans, monitoring adoption, integrating cross-functional inputs, and enabling leadership engagement. The role focuses on preventing fragmentation and ensuring aligned initiatives to enhance organizational change in AI and data efforts.

The AI Adoption & Change Manager drives AI transformation within the IT organization, ensuring user adoption, training, communication, and integration of AI tools into workflows. They build change management plans, conduct assessments, facilitate training, and measure adoption metrics to improve AI initiatives.

The AI Quality Associate evaluates AI outputs for quality, develops benchmarks and rubrics, validates workflows, and collaborates with finance experts to ensure high-quality AI-assisted outputs.

The Senior AI Risk Advisor will lead AI risk assessments, develop governance frameworks, evaluate AI vendors, and engage with stakeholders on AI adoption.

The AI Strategy & Operations Advisor will operationalize AI strategies, build governance frameworks, assess AI maturity, and advise leadership teams.

Design and build clinical-grade automated evaluation pipelines (LLM-as-judge + human review), define benchmarks, run rigorous experiments, partner with engineering to implement CI gates for accuracy, factuality, and safety, and publish findings to improve model robustness and reduce hallucinations.