Top AI Ethics Jobs in Austin, TX

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.

The Senior Manager, Product & AI Policy leads policy development for AI features, collaborating with teams to ensure policies support business objectives and innovation.

The Senior Vice President of AI and Transformation will develop and deliver strategies for integrating AI, analytics, and automation into processes, ensuring they meet regulatory standards. This role involves partnership with leadership to enhance service quality and governance principles for responsible tech use.

The Director of Artificial Intelligence will define, lead, and implement AI strategies, managing R&D, building teams, and ensuring ethical AI use to drive business outcomes.

The Enterprise Architect for Data & AI will shape the architecture for the SaaS platform, ensuring efficient data practices and responsible AI governance, while collaborating with various teams to align technology with business goals.

Lead development and execution of MVB's AI and automation strategy, including Generative and Agentic AI and RPA. Partner with business and technical stakeholders to design, pilot, and scale solutions, establish governance and best practices, monitor performance metrics, manage vendors, and deliver training to ensure ethical, secure, and effective AI deployments.

The Senior Technical Director for AI UX at Rocket Software will define the AI-driven experience across products, focusing on collaborative and responsible AI integration in enterprise workflows. They will establish architectural frameworks, mentor engineers, and validate AI models with customers.

Lead design and implementation of safety and alignment architectures for agentic AI systems; develop monitoring, reward modeling, and human-in-the-loop evaluation; partner with cross-functional teams for responsible deployment; publish research; identify and mitigate failure modes and set safety milestones for production readiness.

The role involves evaluating AI outputs, generating content, providing annotations, and ensuring quality through cultural and contextual awareness.