Artificial Intelligence Jobs in Bellevue, WA

As a Senior Software Engineer II, you'll design and operate a Kubernetes-native platform for AI workload orchestration, improve performance, and mentor engineers.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

The AI Infrastructure Product Marketing Manager leads the product marketing efforts for CoreWeave's AI cloud infrastructure, developing key narratives, messaging, and GTM strategies. Responsibilities include customer engagement, creating marketing materials, measuring impact, and collaborating with sales and engineering teams to drive adoption.

Responsible for architecting and developing AI security infrastructure, optimizing performance and scalability of systems, and driving innovation in a collaborative environment.

The role involves developing high-reliability AI security solutions, enhancing security capabilities, and collaborating on network-related projects, while focusing on scalability and performance.

Lead research and development of LLM and generative AI solutions, design scalable real-time ML models, define priorities and metrics for deep learning and RAG, promote Responsible AI best practices, and drive adoption of new methods across teams.

Lead AI partnership strategy and execution to grow AI Frontier Labs alliances. Own alliance performance, co-sell pipeline, joint GTM and solution development, partner account mapping, enablement, marketing, and executive alignment to scale generative AI adoption and revenue.

As a Solutions Architect at CoreWeave, you will lead technical engagements with customers, build tailored solutions, and collaborate with engineering to enhance products in AI and cloud infrastructure.

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.

The Senior Engineer collaborates with teams, designs and implements technical features, writes production-ready code, and ensures high-quality deliverables, while also engaging in research and learning on new technologies.

The Lead AI Engineer will design and implement LLM-powered applications, optimize RAG pipelines, and evaluate models while integrating AI systems into production environments.

Design and build production-grade full-stack, AI-enabled applications. Develop React/Next.js frontends, backend services on Kubernetes, integrate LLM/AI features, connect data platforms, implement CI/CD, automated testing, observability, and ensure secure, high-performance APIs and services.

Assist the Research Data Science team by analyzing large viewing and demographic datasets, building statistical and machine learning models, developing data pipelines, prototyping AI solutions, and working with engineering and product teams to scale models for measurement and attribution use cases.

Lead and close complex sell-side software and SaaS transactions, draft and negotiate MSAs, order forms and SOWs, advise Sales and Deal Desk on privacy, security, AI, IP and open source issues, partner with cross-functional stakeholders, improve templates and contracting processes, and provide practical legal guidance balancing risk and commercial objectives.

Lead a team building production AI systems for code generation and developer productivity. Define technical roadmap, drive architecture for distributed training/inference and model serving, partner cross-functionally, establish evaluation/observability/benchmarking practices, and hire and mentor engineering talent to deliver scalable, reliable AI-powered developer tooling.

Lead AI-powered analytics product development for Tableau Cloud & Server: define PRDs, manage AI evaluations, partner on GTM, engage customers and field teams, run agile ceremonies, and synthesize customer insights to drive adoption from concept to launch.

Lead and deliver complex, cross-functional technical programs by defining scope, roadmaps, budgets, and KPIs; manage risks, vendors, and resources; ensure compliance with corporate standards; escalate issues to senior leadership and mentor other program/project managers.

Lead development and management of integrated product and service roadmaps for physical and edge AI products. Collaborate with business and technical teams from ideation through integration, define requirements, prioritize features, manage risk, align stakeholders, and coach team members to meet revenue, growth, and product lifecycle objectives.

The engineer will deploy Okta's agent security solutions at customer locations, collaborate with teams, and ensure agent identity and security protocols are met.

Lead enterprise RPA architecture and platform modernization, design scalable API-first automation, define engineering standards and reusable frameworks, integrate AI-assisted engineering, mentor teams, and advise executives on automation strategy and governance to improve reliability and delivery.

The Senior Engineer, Enterprise AI designs and develops AI applications that enhance productivity and decision-making across T-Mobile's enterprise, utilizing various AI frameworks and collaborating with cross-functional teams.

The Principal AI Engineer will design and build agent harnesses, CI/CD pipelines, and evaluation frameworks for autonomous AI agents. Responsibilities include managing infrastructure, optimizing performance, ensuring security, and facilitating continuous improvement in machine learning initiatives at scale.

The Lead/Principal Account SE for Strategic AI will partner with account teams to create AI-driven solutions, demonstrate technical capabilities, and engage with C-suite executives while addressing complex business challenges with Salesforce and OpenAI technologies.

Drive LLM training and inference efficiency by profiling, benchmarking, and optimizing GPU kernels and deep learning systems. Implement latency and resource-use reductions, scale system performance, contribute to agentic frameworks, and publish open-source innovations and research.