Top Machine Learning Jobs in Seattle, WA

Develop and maintain forecasting and machine-learning models for marketing performance, budget allocation, and customer acquisition trends. Diagnose model failures and drift using Python, R, and SQL; conduct scenario forecasting, statistical validation, experimentation, and media attribution analysis. Build dashboards in Tableau and Power BI, collaborate with marketing, finance, analytics, and data engineering teams, and scale models into production environments.

The job description content is missing. Based on the title, this role involves senior-level machine learning engineering. Atlassian offers flexible work arrangements, location-based compensation, benefits, bonuses, commissions, and equity.

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.

Leads technical innovation in data science by developing and deploying advanced analytics, machine learning, deep learning, and generative AI solutions. Responsibilities include preparing complex datasets, evaluating emerging technologies, standardizing modeling toolkits, collaborating on data architecture and governance, guiding technical teams, and communicating data-driven insights to business leaders.

Designs, builds, and operates scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. The role delivers reliable software and infrastructure, improves platform performance and observability, influences architecture and technical standards, and partners with engineering stakeholders to safely integrate AI/ML solutions at scale.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

Build and scale customer-facing generative AI applications for Atlassian Rovo Chat. Develop agent harnesses that improve quality, reliability, and latency; apply advanced machine learning innovations; lead complex projects from technical design through launch; mentor senior engineers; and collaborate cross-functionally with internal teams and customers.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Build and evaluate applied machine learning and Generative AI solutions for product and business problems. Interns will contribute to models, agents, retrieval, experimentation, evaluation, and prototyping with mentorship from experienced engineers. Responsibilities include translating business needs into technical projects, analyzing errors and user feedback, measuring outcomes, and clearly documenting findings, trade-offs, limitations, and next steps.

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.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

This is a senior machine learning engineering role at Atlassian, but the job description contains no specific responsibilities or qualification details. The position offers flexible work arrangements where Atlassian has a legal entity and includes location-based compensation, benefits, bonuses, commissions, and potential equity.

Lead the design, development, deployment, and optimization of production machine learning systems for search, recommendations, personalization, NLP, deep learning, and AI applications. Own projects across the full ML lifecycle, design scalable architectures and evaluation strategies, collaborate with product and engineering teams, translate research into reliable systems, and mentor other engineers.

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.

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.

Design and productionize machine learning systems for personalization, recommendation, ranking, retrieval, and traveler understanding. Lead end-to-end scientific work from problem formulation and feature engineering through experimentation, evaluation, deployment, and monitoring. Partner with engineering, product, and business stakeholders on scalable system integration, influence technical direction, mentor scientists, and advance deep learning, sequential modeling, foundation-model, and recommender-system capabilities.

Build and deploy predictive models, recommendation systems, LLM applications, incentive optimization frameworks, and causal inference models for customer growth. Partner cross-functionally to improve acquisition, engagement, retention, and expansion. Measure marketing impact, guide investment decisions, develop AI analytics and decision systems, and provide technical leadership and mentorship across data science initiatives.

Design, prototype, and evaluate machine learning models; develop maintainable training and inference code; conduct data preparation, feature engineering, statistical analysis, and error analysis; and contribute to ML system, API, and data modeling. Collaborate with scientists, engineers, mentors, and product partners to define metrics and build production-aware, fair, and scalable ML solutions.

Build and operate secure, scalable AI/ML platforms across hybrid and on-premises environments. Develop data pipelines, MLOps workflows, Kubernetes deployments, CI/CD and GitOps automation, observability, infrastructure-as-code, and secure container supply chains. Support model serving, generative AI, and retrieval-augmented generation while troubleshooting distributed systems, improving reliability, and contributing to architecture, standards, documentation, incident response, and technical mentorship.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.