Top AI Ethics Jobs in Seattle, WA

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.

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.

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.

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.

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.

Lead end-to-end technical programs for AI safety research and infrastructure: scope plans, prioritize across teams, build compute/resource roadmaps, enable technology transfer, and coordinate internal stakeholders to accelerate research delivery.