Top AI Jobs in Seattle, WA

Lead product strategy and lifecycle for AI infrastructure platforms, prioritizing scalability, resiliency, compliance, and customer value. Drive discovery, market research, and backlog management; define KPIs (cost, reliability, risk posture); partner with engineering and cross-functional teams to deliver GPU-enabled model training, inference, and workload scheduling solutions for enterprise clients.

Lead Boeing’s enterprise AI governance program by designing intake, review, risk-tiering, approval, and escalation workflows. Develop AI policies, standards, documentation, training, metrics, and executive reporting. Conduct AI risk assessments, evaluate vendor and generative AI systems, monitor regulatory requirements, and coordinate with engineering, legal, compliance, cybersecurity, and business stakeholders to ensure responsible, auditable AI adoption.

The Staff Forward Deployed Engineer drives AI adoption for strategic customers by solving complex cloud infrastructure challenges, developing scalable assets, and influencing product roadmaps through collaboration and technical expertise.

The Applied Scientist will tackle various machine learning problems, enhance valuation systems, and deploy deep learning models while collaborating with cross-functional teams.

Lead and oversee multiple multidisciplinary teams to architect, develop, deploy, monitor, and maintain full-stack AI solutions (including LLM integrations). Provide strategic and technical leadership, drive experimentation and CI/CD practices, ensure quality and stakeholder alignment, promote innovation, and mentor senior and junior team members to deliver business-aligned AI outcomes.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Lead and deliver the agentic AI roadmap for Azure Supportability. Align cross-functional teams, define requirements and OKRs, quantify AI impact, conduct research and competitive analysis, manage stakeholders, and address technical challenges to improve customer experience and operational efficiency.

Develop, test, and validate Generative AI agents and data pipelines with automated testing and governance controls. Analyze complex problems, produce actionable insights, mentor junior associates, and collaborate with multidisciplinary teams to deliver AI-enabled client solutions while upholding professional standards and documentation.

Design, build, and operate production-grade AI tools, automations, and integrations for HR workflows. Lead LLM-based solutions (prompting, RAG), integrate people systems (Workday, Slack, Google Suite), improve data quality, support end users, and drive measurable productivity and product roadmap decisions.

The Director leads engineering and research for AI applications, managing interdisciplinary teams, driving technical roadmaps, and fostering collaboration across functions to deliver impactful AI initiatives.

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.

Develop and test AI/ML software solutions in collaboration with engineering, product, and program teams. Apply engineering principles, responsible AI practices, and emerging machine learning techniques to build reliable, efficient, observable products. Responsibilities include understanding user requirements, learning new technologies, improving technical solutions through feedback, and completing software projects in a collaborative environment.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead design, build, and operate secure, scalable cloud platforms optimized for AI/ML. Provide technical guidance, develop production code, implement IaC and CI/CD, optimize cloud performance/costs, drive AI-assisted engineering governance, and partner with AI teams to translate compute needs into infrastructure requirements.

Lead end-to-end delivery of document extraction and NLP solutions for payments: design scalable model pipelines, develop extraction/classification models, define evaluation and lifecycle practices, ensure compliance and explainability, and partner with cross-functional stakeholders to productionize and monitor ML systems.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Lead firmwide technology strategy for Data & AI, build partnerships with cloud and infrastructure vendors and the venture ecosystem, drive innovation adoption, evaluate and scale new technologies, raise internal awareness of Data & AI opportunities, support client-facing business development and strategic investments, and influence senior stakeholders to align partner roadmaps with JPMC objectives.

Develop, train, optimize, and deploy LLMs and multi-agent AI systems for tactical edge hardware. Interface agents with sensors, effectors, and drones, benchmark performance, and build reliable fail-safe production systems for defense customers.

The AI Engineer IV will lead the development and implementation of AI systems, influence architecture decisions, mentor team members, and ensure best practices in a hybrid work environment.

Lead AI engineering teams to build and scale AI-powered solutions while managing execution across multiple teams and collaborating with cross-functional partners.

Enterprise hunter role selling AI data solutions (data collection, annotation, LLM fine-tuning, generative AI) to Fortune 500 clients. Build pipeline, close high-value accounts, act as consultative SME on AI data preparation and workflows, and partner with solutions architects and PMs to ensure successful delivery while exceeding revenue targets.

The Research Engineer will develop training environments for agentic AI, balancing research and engineering tasks while collaborating with teams.