Top AI Ethics Jobs in Austin, TX

As a Technical Product Intern, you'll prototype user safety features, conduct competitive analysis, and translate compliance frameworks into product logic, focused on real-time AI enforcement.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Lead firmwide AI enablement by setting strategy and roadmap, designing multi-level training, running an AI champion community, producing adoption analytics, shaping governance, evaluating tools and vendors, and managing stakeholders and a small team to drive responsible AI adoption and measurable business impact.

The Software Architect will lead AI quality evaluation, design evaluation frameworks, create risk controls, and ensure compliance, partnering with various teams on AI systems.

Work with clients to drive enterprise AI adoption: assess readiness, build adoption roadmaps, design and deliver training and enablement, run champion programs, manage communications, measure value with dashboards, and implement Responsible AI practices and risk management.

Design, build, and govern a centralized knowledge foundation for enterprise AI. Audit, structure, and optimize content for retrieval and AI use; define taxonomy and content standards; capture institutional knowledge; partner with stakeholders to keep knowledge current and reusable; improve retrieval quality and governance for downstream AI applications.

Lead enterprise fraud risk strategy and entity-level fraud state estimation. Design rule/model/hybrid decisioning, translate signals into decision support, partner with risk, operations, and product, and drive measurable fraud reduction while ensuring explainability and governance.

Design, curate, and evaluate high-quality multimodal datasets for foundation models. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; collaborate with Safety, Ethics, and research teams to ensure data quality, privacy, and responsible AI alignment.

Evaluate AI-generated responses for business users by comparing outputs against professional email history, assessing relevance, accuracy, personalization, and providing detailed feedback to improve models while maintaining data privacy.

Lead design and scaling of legal AI evaluation frameworks and benchmarks, build and maintain datasets, audit and score AI legal outputs, define metrics focused on reasoning and citation accuracy, and collaborate with engineering to translate legal errors into model fine-tuning feedback.