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.

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.

Maintain and audit high-resolution image datasets, correct labels and classifications, support data annotation quality, diagnose and validate deep learning model issues, perform field tests, document results, and collaborate with support and field teams to prioritize fixes that improve product reliability and customer satisfaction.

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.

Review AI-generated compensation analyses and recommendations for accuracy, completeness, and business relevance. Identify errors and gaps, provide actionable written feedback, follow scoring frameworks, document findings, and collaborate to refine evaluation methods.

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.

The AI Development Specialist engages with enterprise developer teams to sell coding assistants and AI solutions, co-sells with NVIDIA, and proposes consulting engagements, focusing on improving developer productivity and application performance.

The Senior Solutions Architect will lead prototyping for AI partners, architect AI systems, resolve technical issues, and produce reusable assets to enhance enterprise AI adoption.

Lead evaluation, piloting, and rollout of AI-enabled tools and automated workflows for legal teams. Collaborate with attorneys, practice leaders, and tech partners to design solutions, drive adoption, deliver training, and measure impact to improve legal service delivery and internal operations.

As an AI Operations Specialist, you will evaluate AI model outputs, annotate data, and ensure the quality and effectiveness of Fetch's AI systems, using tools like ChatGPT and Google Sheets.

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.

Research, deploy, and maintain AI models and automation solutions; integrate systems and APIs; manage AWS infrastructure and PostgreSQL databases; build BI dashboards; monitor and optimize AI systems and provide technical support.

Contribute to generative AI model training and evaluation by designing and solving ML infrastructure and systems challenges. Build and optimize distributed training, custom GPU kernels, evaluation frameworks, and provide technical reviews and feedback to improve training data and model capabilities.

Evaluate and improve AI-generated Tagalog text for linguistic accuracy, grammar, and cultural relevance. Provide rewritten responses, craft prompts/responses, perform grammaticality checks and preference ranking, and collaborate with global teams to enhance conversational AI datasets.

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.

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.

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.

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

Perform on-site data annotation and quality assurance for natural language datasets, test AI products, analyze data for consistency and model performance, report anomalies and insights, and collaborate to refine machine learning models.

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.

The AI Implementation Specialist will onboard and train customers on AI design software, providing support and creating educational resources, while focusing on user feedback and software adoption.

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.

The role involves designing and maintaining automated workflows, developing AI agents, integrating APIs, and optimizing automation systems while collaborating remotely with teams.