Top AI Ethics Jobs in Austin, TX

Lead a multidisciplinary red teaming team to design and run adversarial tests, evaluate model risks, produce high-quality deliverables, and communicate findings to clients while improving methodologies and workflows.

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.

Lead enterprise AI governance, risk management, and Responsible AI adoption. Oversee AI inventory, policy, risk lifecycle, regulatory compliance, vendor assessments, and integration of governance into business workflows. Partner with Legal, Compliance, Security, PMO, and business units to prioritize AI initiatives, report metrics and ROI, and advise executives and boards.

Sell AI services and SaaS to new and existing B2B clients across industries, build pipeline, manage accounts, record CRM data, present complex proposals, and explain AI regulations and standards (e.g., 42001, 42006). Maintain client relationships and ensure timely delivery and retention.

Design, implement, and evaluate applied research projects to improve alignment, safety, and evaluation of LLM-based guardrails. Work on experiments (e.g., synthetic data quality metrics, explanations for blocked content), deliver results with mentor guidance, and present findings to the team to shape product capabilities.

Perform human review and annotation tasks to train and evaluate AI: assess prompts and outputs, correct text, analyze multimedia, record voice samples, and provide structured annotations and culturally informed feedback following guidelines.

Conduct red teaming and adversarial evaluations of AI systems across violence, extremism, child safety, grooming, crisis, and other harm categories. Design evaluation frameworks, test scenarios, scoring criteria, taxonomies, and interventions that translate real-world harm expertise into structured technical outputs. Review model responses, exercise expert judgment, and produce methodology and findings for technical and government audiences while meeting ethical, legal, contractual, and data protection obligations.

Lead cross-functional teams to accelerate secure enterprise AI adoption. Define AI security standards, chair AI governance, drive AI FinOps and vendor risk management, deploy automated guardrails to prevent data leakage, and translate business needs into technical roadmaps while partnering with GRC, Legal, Finance, and Product.

Design HCI standards for Agentic AI products: create transparency and human-in-the-loop workflows, dynamic/generative and multi-modal interfaces, trust/error UX for hallucinations, prototype autonomous sequences with tools like Figma/Protopie, and organize dense information for auditability and oversight.

Own decision quality by evaluating AI-generated decisions, identifying reasoning failures, designing feedback and training loops, defining decision-quality metrics, and collaborating with Product, R&D, and Engineering to improve robustness, explainability, and drift detection.