Top AI Specialist Jobs in Seattle, WA

Assess AI-enabled products and software against regulatory requirements and standards, supporting conformity assessments, CE marking, certification, and client guidance. Review technical documentation, mentor colleagues, develop assessment procedures and tools, contribute to AI testing methodologies, and support accreditation, quality, and continuous improvement activities. The role requires practical AI/ML experience, regulatory knowledge, strong analytical skills, and collaboration across global multidisciplinary teams.

Author and verify rigorous multiple-choice computer science assessment items across many CS domains to create gold-standard benchmarks for AI research. Produce correct answers, nine plausible distractors, solution explanations, difficulty labels, and authoritative references. Review and edit questions for technical accuracy, clarity, and reproducibility. Contribute asynchronously as an independent contractor, ~10+ hours/week, fully remote.

Review, localize, fact-check, translate, and adapt football-related content (EN→FR-CA). Conduct market research on terminology and trends, provide copy-editing and feedback, and evaluate/MTPE machine translations to ensure native-level quality and adherence to style guides.

Review, localize, translate, fact-check, and adapt football-related content from English (US) to Dutch, following style guides. Perform quality control, MT post-editing and evaluations, conduct market-specific research, and provide feedback to improve localized content for Dutch audiences.

Review, localize, fact-check, translate, and adapt football-related content for Croatian audiences; perform MT post-editing and quality control; conduct market-specific research on terminology and trends; provide feedback and adhere to style guides.

Design, deploy, and operate a secure, scalable Generative AI platform on AWS (Bedrock) for production LLM applications. Own architecture, deployment, monitoring, incident management, reliability, governance, and cost optimization in regulated financial environments.

Design, build, and operate a secure, scalable Generative AI platform on AWS (including Bedrock) for production LLM applications in regulated environments. Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and cost optimization. Implement RAG, Agents, inference, guardrails, observability, access controls, and governance, with emphasis on Responsible AI and production reliability in financial services.

Design, build, and operate a secure, scalable enterprise Generative AI platform on AWS (including AWS Bedrock). Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and governance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Support production-grade GenAI applications in regulated/financial environments.

The role involves designing and delivering AI training programs, creating learning materials, and facilitating workshops to enhance user understanding of AI tools within enterprises.

Lead sales and deployment of custom AI/ML solutions, driving pipeline and revenue for allocated accounts. Collaborate with Field Sales, Product, Sales Engineering and AI teams to discover requirements, architect agentic AI solutions, write SOWs, negotiate commercial terms, and manage complex sales cycles to ensure customer adoption and measurable business outcomes.

Optimize and compress large language and vision models for on-device inference. Build model distillation and hardware-specific compilation pipelines, and benchmark performance across various NPU/GPU architectures to enable efficient edge deployment.

Develop intent-recognition models from gaze, gesture, and voice data; implement socially aware navigation that respects personal space; and design visual and auditory feedback for robot user interfaces. Run user studies and rapidly prototype AI-driven voice and visual interfaces.

Design and implement high-fidelity spatial mapping and real-time object recognition pipelines; integrate vision outputs with robotic manipulation and navigation systems; and train and fine-tune Vision-Language-Action models to improve robotic autonomy and perception.

Perform adversarial testing on language models and multimodal agents, implement guardrails and real-time filters for autonomous tools, and develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Design, develop, and deploy Agentforce AI agents and custom actions on the Salesforce platform. Build Apex classes, LWCs, flows, and API integrations with Data Cloud and legacy federal systems. Enforce AI safety, federal security/compliance, support testing/UAT, and enable secure DevSecOps/CI/CD deployments in cleared environments.

Design, develop, and deploy Agentforce AI agents and custom actions on Salesforce. Build Apex, LWCs, flows, and secure API integrations with legacy federal systems and Data Cloud. Ensure AI safety, compliance, testing, and support CI/CD/DevSecOps practices in cleared federal environments.

Designs, builds, tests, and deploys AI-enabled automations and copilots for GTM and RevOps. Creates reusable agent templates, prompt libraries, and orchestration patterns; implements end-to-end API integrations; monitors performance; documents and hands off production solutions; and trains/coaches business teams on safe, responsible AI usage and prompting best practices.

The role involves cultivating technical relationships with Accenture, supporting solution development, customer deal support, and representing Databricks at events.

Review machine-translated content into Finnish by comparing source and translation, making pass/fail quality judgments on accuracy, grammar, and naturalness. Follow guidelines and provide consistent assessments during a one-year, 1–5 hour/week freelance engagement.

Create, optimize, schedule, and publish blog and social content about AI applications. Test headlines, landing pages, and ads; review analytics, predict opens/clicks/conversions, write performance reports, and personalize curated content.

Design and maintain a skill and concept graph, build ML models to infer learner skills, and develop recommendation systems and LLM-driven features. Ship production-ready LLM systems, define explainability standards, partner with frontend and product teams, and monitor model performance and evaluation frameworks.

The AI Adoption Specialist guides organizations in integrating AI technologies, developing strategies, conducting pilots, and overseeing deployment while monitoring performance.

Design, build, and operate a secure, scalable Generative AI platform on AWS (including Bedrock) to support production LLM applications in regulated financial environments. Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and performance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Provide production support, reliability engineering, and AI governance.

Define and maintain benchmark datasets and gold standards, create training and certification materials, monitor annotator drift, perform double-blind quality audits, and act as a high-trust annotator for critical production labeling. Collaborate with Computer Vision and Data Ops to improve the quality pipeline and scoring models.

The role involves designing and implementing automation systems, improving workflows, and developing tools for sales support using AI and automation platforms.