Top AI Ethics Jobs in Austin, TX

Evaluate and improve AI-generated outputs for accuracy, clarity, quality, and business alignment. Develop and refine prompts, annotate data, conduct rubric-based assessments, fact-check content, and provide actionable feedback on model performance. Create and edit technical documentation, strategy reports, market analyses, business cases, executive summaries, and professional communications. The role requires strong analytical reasoning, writing, research, quality assurance, and attention to detail.

Develops and maintains AI governance policies, frameworks, and controls aligned with regulatory and industry standards. Conducts AI model risk assessments covering bias, explainability, data quality, and security; evaluates organizational and vendor risks; supports audits; designs controls; and partners with Legal, Privacy, Security, and Technology teams. The role also promotes compliant AI practices and organizational awareness while applying advanced AI/ML knowledge.

Lead AiLO portfolio integrating AI into K-12 systems: set strategy, design and draft high-stakes deliverables, manage complex projects, mentor teams, cultivate partnerships, and represent ILO externally to scale responsible AI adoption in education.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.

Works with enterprise clients to drive AI adoption by assessing readiness, identifying use cases, building adoption roadmaps, delivering training, and managing communications and champion programs. Develops prompt guides, AI-enabled workflows, dashboards, and adoption metrics. Facilitates workshops and office hours, supports responsible AI and safety practices, maintains risk registers, and creates reusable resources to scale and sustain AI usage.

Evaluate AI models for dangerous CBRNE-related outputs, designing adversarial prompts, multi-turn attack chains, and threat models. Assess technical accuracy and real-world risk, distinguish open-source information from harmful uplift, score responses using severity rubrics, and document failures. Collaborate with AI researchers and policy teams to improve model safeguards and evaluation frameworks while following strict ethical and operational security protocols.

Leads research and thought leadership on AI-led go-to-market strategy and business transformation for small and midsize technology providers. Advises CEOs and executive teams on revenue architecture, sales, marketing, customer success, partnerships, pricing, operations, and converging AI technology stacks. Conducts market analysis, predicts trends, presents recommendations to clients and conferences, supports sales, reviews research, mentors colleagues, and improves research processes.

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.

Design and test challenging prompts based on complex professional or personal workflows. Identify AI failure points by iterating prompts in ChatGPT, then create a comprehensive grading rubric for evaluating model outputs. Participants must share their screen, camera, and microphone during the recorded trial and submit the prompt, failure notes, rubric, and generated output file. Successful participants may be considered for ongoing AI evaluation work.

Create clear, structured rater guidelines, evaluation rubrics, and reviewer instructions for human evaluators across domains. Review and revise documentation to remove ambiguity, design scoring frameworks, handle edge cases, and collaborate with researchers and SMEs to ensure consistent, high-quality AI evaluations.