Top AI Jobs in Seattle, WA

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Conduct an AI-focused research project and contribute tools, pipelines, and systems for Atlassian’s Research and Insights organization. The intern will help develop AI-enabled research workflows, collaborate with a research manager, and support faster insight generation for cross-functional teams. The 12-week, full-time internship is based at Atlassian’s Seattle hub and includes mentorship, technical training, and professional development.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Evaluate AI-generated outputs for clinical accuracy, safety, and appropriateness across patient and provider products. Apply and refine evaluation rubrics, document clinical reasoning, participate in calibration and inter-rater reliability reviews, provide structured feedback to Clinical Product teams, label datasets, and review product use cases. Work within designated systems while maintaining HIPAA compliance and patient confidentiality.

Lead design and adoption of cloud-based, portable AI platform architectures across hybrid environments. Advise on technology selection, document target-state designs, shepherd architecture reviews, develop multi-year roadmaps, integrate enterprise AI capabilities, produce secure production code, and align engineering with architectural governance and controls.

Leads multidisciplinary teams delivering AI-enabled, cloud-native applications on AWS. Oversees architecture, development, testing, deployment, modernization, platform engineering, security, reliability, and client advisory activities. Translates business objectives into technical roadmaps, guides complex engineering work, coordinates cross-functional architecture reviews, communicates delivery risks and options, and coaches teams across multiple workstreams.

Designs, builds, tests, and troubleshoots full-stack cloud-native applications on AWS. Translates client needs into technical solutions across architecture, development, testing, and delivery; builds scalable infrastructure and integration patterns; applies AI-enabled engineering methods; and supports modernization initiatives. The role also reviews technical work, mentors junior engineers, collaborates with architecture teams, and helps deliver cloud, data, and analytics solutions.

Leads AWS-native application design, development, testing, modernization, and architecture efforts. Manages technical workstreams, delivery timelines, scope, and team allocation while coaching engineers on cloud infrastructure, AI-native engineering, testing, and troubleshooting. Reviews technical designs and code, coordinates application and infrastructure activities, supports client architecture discussions, and improves delivery reliability and speed.

Architects the data and AI foundation for marketing, sales, and analytics. Responsibilities include building Databricks pipelines, Gold Layer tables, semantic models, documentation, AI-enabled data products, and production LLM or agent systems. The role leads cross-functional initiatives, translates ambiguous business needs into scalable solutions, ensures data quality and reliability, mentors engineers, automates workflows, and partners with BI, data science, marketing, sales, and R&D teams.

Develop and implement advanced machine learning algorithms and models for Atlassian products. Design system and model architectures, conduct experiments and evaluations, collaborate with product, engineering, and analytics teams, and mentor emerging machine learning engineers. The role also contributes to shared AI infrastructure and capabilities supporting search, knowledge discovery, and conversational experiences across the company.

Lead technical strategy and development of Confluence AI experiences across frontend, backend, and AI integrations. Design scalable systems, apply generative AI, retrieval, agents, evaluation, and safeguards, and improve product quality, adoption, trust, and customer value. Drive complex initiatives from architecture through launch, partner cross-functionally, mentor engineers, establish quality practices, and guide experimentation and continuous improvement across Atlassian.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Lead enterprise AI engagements from opportunity discovery through production deployment, adoption, measurement, and continuous improvement. Design scalable AI systems, evaluate models and agentic architectures, establish engineering and responsible AI standards, and create reusable frameworks. Partner with executives and business teams, translate ambiguous problems into measurable outcomes, influence technical direction, and coach senior engineers and emerging technical leaders.

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Leads the vision, strategy, and roadmap for AI-powered Brand Expedia experiences across trip inspiration, planning, booking, and travel. Owns experimentation, business outcomes, responsible AI standards, model evaluation, platform partnerships, and third-party AI integrations. Collaborates with engineering, science, design, marketing, and brand teams while coaching product managers and shaping scalable consumer experiences across Expedia’s portfolio.

Build and ship AI-powered tools, applications, analyses, and data pipelines for service operations. Prototype with large language models, define success metrics and safeguards, translate operational problems into technical requirements, and represent operations in cross-functional AI programs. Lead workstreams, guide partner-based AI builders, assess risks and recommendations, and support adoption of tools across operations teams.

Define and drive CrowdStrike’s company-wide AI and agentic architecture. Design production AI systems, shared platforms, lifecycle workflows, and developer tooling across products and business units. Improve performance, resilience, scalability, observability, governance, security, and auditability across cloud and edge environments. Build reference implementations, guide architectural decisions, resolve complex engineering challenges, mentor technical leaders, and align senior stakeholders around AI strategy and implementation.

Design, deploy, optimize, and scale production AI and agentic workloads for strategic customers on DigitalOcean’s AI-Native Cloud. Build benchmarking systems, deployment frameworks, automation tooling, starter kits, and operational playbooks. Optimize inference performance, GPU efficiency, latency, reliability, and cost across distributed systems. Validate new platform capabilities, influence product roadmaps, collaborate with vendors and partners, and enable customer-facing teams through reference architectures and technical guidance. Travel up to 30% for customer engagements, workshops, conferences, and collaboration.

Designs, builds, tests, and deploys cloud-based AI applications and LLM-powered agentic systems. Works directly with customers to address pain points, documents experiments, communicates research results, and mentors early-career team members. The role combines applied research, product development, production deployment, and customer-driven innovation.

Leads a team developing AI capabilities for CrowdStrike’s cross-platform endpoint sensor. Owns the AI-on-endpoint roadmap, including machine learning model operationalization, telemetry data pipelines, on-device inference, architecture, feature delivery, and cross-functional collaboration. Manages hiring, mentoring, engineering quality, testing, performance, and releases across Windows, macOS, and Linux sensor platforms.

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.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Leads forward deployment of agentic AI systems within a business unit. Designs and delivers production LLM solutions, including integrations, RAG pipelines, prompt orchestration, multi-agent workflows, data engineering, and performance experiments. Measures ROI, improves data quality, mentors engineers, guides technical delivery, and communicates with business stakeholders. The role requires strong Python, cloud-native deployment, enterprise integrations, responsible AI, and data security expertise.

Design and build MLOps infrastructure for production deployment of data, machine-learning models, and software. Develop end-to-end ML pipelines, CI/CD/CT workflows, secure repository strategies, containerized deployments, and automation for Edge AI applications. Collaborate with developers, data scientists, and ML engineers to define tooling, monitor DevOps performance, and improve operational efficiency. Support embedded-system CI/CT, open-source pipelines, and model testing.