Top AI Specialist Jobs in Seattle, WA

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Build, test, document, launch, and support AI-powered tools, prompts, skills, agents, automations, and workflows for DC Operations. Partner with field technicians to gather requirements, maintain intake/tracking, evaluate opportunities, facilitate training, provide day-to-day support, track tool usage and impact, and convert field-built solutions into documented, supported assets.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Lead go-to-market and technical strategy for AI developer tooling, winning early customers and building repeatable playbooks. Translate customer MLOps requirements into product feedback, partner with engineering and Weights & Biases, and enable sales/solutions teams to close deals around experiment tracking, model governance, and LLM observability.

Lead organization-wide strategy for generative AI applications, including prompt engineering, agentic workflows, RAG pipelines, evaluation frameworks, developer tooling, safety guardrails, and LLM architectures. Ship production LLM products, conduct technical reviews and red-teaming, optimize cost, latency, and quality, establish reusable standards, and mentor engineers and applied scientists across teams.

Evaluate and implement AI and automation solutions for legal workflows; lead pilots and rollouts; collaborate with attorneys and tech teams; create training and change management materials; identify process improvements to increase efficiency across practice areas.

Build and deploy AI-powered business workflows across B2B SaaS companies. Responsibilities include transforming business processes, implementing automation and enterprise systems, creating dashboards and KPI reports, integrating APIs, supporting UAT, improving data quality, documenting procedures, and driving user adoption across Sales, Marketing, Support, HR, Finance, and Operations.

Design and deliver GenAI solutions: prototype to production LLM services, build RAG pipelines, fine-tune models (LoRA/QLoRA/PEFT), engineer prompts/tools/memory, ensure safety and evaluation, deploy with MLOps (CI/CD, containers, IaC), optimize inference, and ensure security/compliance while collaborating with product, data, and SRE teams.

Lead design, validation, and continuous improvement of AI-assisted measurement and learning systems for K-12 assessments. Translate measurement intent into reproducible AI workflows, prototype AI-assisted content generation, ensure outputs meet validity, fairness, accessibility, and instructional standards, design validation and monitoring frameworks, partner with cross-functional teams (AI Science, Technology, Content), and contribute research, documentation, and thought leadership on intelligent assessment and learning systems.

Design, build, and deploy production AI systems and integrations (LLM-based) for law-firm clients. Create connectors, automation workflows, agents, and enablement programs, embed AI into client processes, and support internal AI adoption.

Design realistic, specialty-specific clinical case scenarios and gold-standard attending-level responses, evaluate and grade AI-generated outputs using structured rubrics, provide detailed written feedback to improve model clinical reasoning, and participate in onboarding, calibration, and review sessions in a fully remote, asynchronous contractor role (~20 hours/week).

PhD-level physicists will design challenging graduate-level physics problems and contribute high-quality datasets to improve AI model reasoning, ensuring scientific rigor and consistency while collaborating remotely and asynchronously.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Join an internal AI enablement team to assess workflows, build prompt-based automations and lightweight apps, coach business teams, test and document tools, track user skill development, research AI platforms, and ensure governance and security compliance.

Drive pricing analytics, margin optimization, competitive intelligence, and AI adoption across Commercial Management. Develop pricing models, predictive analytics, AI-assisted recommendations, dashboards, and executive reporting to improve profitability, forecasting, governance, and commercial decisions. Automate reporting and workflows, monitor competitor and deal intelligence, identify margin leakage and growth opportunities, and present actionable insights to senior leadership.

Audits AI-generated support responses for accuracy, completeness, hallucinations, compliance, and customer trust. Governs knowledge sources, identifies outdated or conflicting content, performs root-cause analysis, and builds performance dashboards. Partners with Content, Product, Engineering, and Support teams to improve prompts, workflows, guardrails, documentation, and AI training. Establishes scalable QA frameworks and closed-loop optimization processes for AI support experiences.

Build AI agents, Python automations, analytics tools, dashboards, and data products that improve telecom product quality, reduce fraud and billing errors, and optimize margins. Analyze complex datasets using SQL, perform root-cause analysis, establish KPIs, and enable self-service insights. Partner cross-functionally with Carrier Relations, Engineering, Product, Supply Operations, and Finance to define requirements, governance, and scalable workflows.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Design, build, and operate a secure, scalable GenAI platform on AWS (including Bedrock). Own platform lifecycle, enable LLM inference, RAG, Agents and guardrails, ensure observability, incident management, security, compliance, cost optimization, and reliability for production-grade regulated-financial environments. Provide Python-based development and support for microservices and REST APIs.

Lead complex, cross-functional AI programs end-to-end: translate strategy into integrated plans, drive multi-team execution, mitigate risks (data, model, compliance), apply ML lifecycle practices, use Agile/kanban methods, manage budgets/vendors, communicate to senior stakeholders, and mentor other program managers to improve AI program standards.

Own strategy, design, and optimization of Vanta's customer-facing chatbot and internal AI Copilot. Build and maintain AI-driven knowledge, design conversation flows in Intercom, analyze performance data, and collaborate cross-functionally to increase resolution rates and CSAT. Handle a small volume of support tickets to inform improvements and maintain product expertise.

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.