Top Tech Jobs & Startup Jobs in Seattle, WA

Manages accounts receivable collections, delinquency resolution, billing disputes, payment plans, cash application, refunds, reconciliations, aging analysis, bad-debt provisioning, reporting, audit support, and process improvements. Collaborates with global customers, partners, accounting teams, sales operations, and management while maintaining collection KPIs and supporting team development.

Own financial planning, forecasting, budgeting, and modeling for Atlassian’s AI infrastructure and token spend. Partner with Engineering, Product, Business Operations, and Finance leadership to evaluate AI economics, usage, vendor pricing, infrastructure requirements, COGS, unit economics, and gross margins. Drive scenario analysis, long-range planning, process improvements, and scalable financial models while translating complex technical and financial data into actionable executive insights.

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.

Security Engineering Intern responsible for completing a scoped security project, developing automation and investigation tools, researching vulnerabilities, analyzing findings, validating AI-assisted outputs, collaborating with engineers, and documenting and demonstrating solutions. The role requires programming, web and systems knowledge, foundational security understanding, AI familiarity, and current enrollment in a bachelor’s or master’s program.

Own customer-facing products end to end across the full stack, from React and TypeScript interfaces to backend services and APIs. Shape product and architecture decisions, use telemetry to measure outcomes, and build AI-enabled and agentic experiences with LLMs. Set technical direction, collaborate with product and design teams, mentor engineers, and drive ambiguous prototypes into reliable, scalable production systems.

Conduct statistical analysis, evaluate product experiments, analyze user interaction data, identify growth opportunities, and build dashboards, notebooks, or AI agents. Partner with data science, engineering, and business teams to communicate actionable insights. The internship requires full-time participation for 12 weeks in the Seattle hub and enrollment in a master's program with graduation by August 2028.

Own customer-facing features end to end across React/TypeScript front ends, backend services, and APIs. Make product-driven architecture decisions, use telemetry to improve outcomes, and build AI-enabled and agentic experiences with LLMs. Set technical direction, influence product strategy, mentor engineers, and drive ambiguous prototypes into reliable, scalable production systems.

Own customer-facing product features across the full stack, from ambiguous problems through measurable outcomes. Make architecture and product decisions, use telemetry to guide design, and build AI-enabled and agentic experiences integrating LLMs. Set technical direction, influence product strategy, mentor engineers, and drive prototypes into reliable, performant production systems at scale.

Lead architecture and delivery of a secure, low-latency Kubernetes-based compute platform for AI agents and untrusted workloads. Own isolation, scheduling, lifecycle, networking, policy, observability, and control-plane capabilities across GCP and AWS. Drive cross-team outcomes from design to adoption, improve developer experience via APIs and automation, and champion reliability, security, performance, and cost efficiency while mentoring engineers and setting technical direction.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.