Top AI Ethics Jobs in Austin, TX

Lead enterprise AI transformation and architecture engagements from strategy through delivery. Advise executives on AI roadmaps, design end-to-end AI and data architectures (including knowledge graphs, data catalogs, RAG, and integrations), establish governance and responsible AI practices, drive adoption and change management, and develop reusable practice assets and thought leadership to realize business value.

Design and implement state-of-the-art RL algorithms and scalable training pipelines for whole-body locomotion and manipulation; transfer and fine-tune policies from simulation to physical humanoid robots; build motion retargeting from human demonstration; collaborate with hardware and controls teams to diagnose system-level issues and demonstrate progress on robot hardware.

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

Lead ownership of reliability, evaluation, and safety for an AI copilot in a trading platform. Build eval systems, benchmarks, automated quality gates, monitoring, and model improvement loops; partner with engineering and product to define safe tool contracts and ensure predictable, auditable assistant behavior in trading workflows.

Lead strategy and architecture for the enterprise knowledge/context layer powering AI: set standards, control models, connector strategy, and federated operating models. Ensure content is AI-ready, permissions-aware, and measurable for retrieval and grounding. Partner with IT, Engineering, Legal, Privacy, and Security to define eligibility, sensitivity, and lifecycle policies and drive adoption across domains.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Design and deliver custom AI-driven legal workflows, implement and scale post-sales solutions, define KPIs and success metrics, inform product strategy, partner with Customer Success to demonstrate ROI, and maintain objective, ethics-compliant processes and playbooks for plaintiff law firms.

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.

Lead manual QA for UI and API surfaces, design and execute test plans in TestRail, use Claude AI and MCP for test generation and analysis, own regression and release validation, perform advanced API testing with Postman, triage defects in JIRA, mentor junior QA engineers, and improve SDLC testability and processes.

Lead implementation and operationalization of Responsible AI and AI governance across the AI lifecycle, embedding controls into delivery processes, assessing vendor and model risk, monitoring performance and drift, and partnering cross-functionally to translate regulations into policies, playbooks, and training that enable safe, ethical, and compliant AI adoption.