Top Tech Jobs & Startup Jobs in Seattle, WA

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.

Senior Software Engineer role at Atlassian with flexible work-location options. The detailed job responsibilities and qualifications were not provided. Compensation ranges from $149,400 to $235,000 in U.S. geographic pay zones, with potential benefits, bonuses, commissions, and equity.

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.

Build and operate scalable machine learning systems for personalized recommendations, growth journeys, cross-product expansion, and sales experiences. Design models, datasets, features, evaluation frameworks, and decisioning services; run offline policy evaluations and online experiments; and monitor attribution, latency, quality, and fairness. Partner with product, engineering, data science, analytics, marketing, and sales teams to turn ambiguous growth opportunities into measurable production capabilities and strategic ML initiatives.

Build enterprise-scale machine learning and agentic search systems enabling AI agents to discover, retrieve, evaluate, and synthesize information. Work spans retrieval, ranking, LLM reranking, multi-step search, tool intelligence, grounding, citations, evaluation, and production infrastructure while addressing permissions, freshness, latency, trust, and multi-tenant requirements.

Lead Atlassian’s TDP SQL engineering team, owning people leadership, platform strategy, roadmap execution, reliability, security, compliance, incident response, technical direction, cross-functional dependencies, infrastructure economics, and operational outcomes. Build a high-performing organization and evolve a scalable managed relational database platform across multiple cloud environments while supporting enterprise requirements such as data residency, encryption, disaster recovery, and tenant isolation.

The job description does not include specific responsibilities or qualification details. The role is identified as a Senior Machine Learning Engineer at Atlassian, with flexible work location options and a U.S. base salary range varying by geographic pay zone.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

Provides technical leadership on complex software projects from design through launch, addressing architecture, reliability, security, testing, and observability at scale. Leads company-wide engineering initiatives, establishes technical standards, mentors engineers, and promotes engineering excellence. Requires extensive full-stack development experience, strong communication, systems engineering expertise, customer-focused decision-making, and familiarity with A/B testing.

Leads technical strategy, architecture, and engineering execution across Atlassian’s Rovo Chat, Search, Growth, Apps, and Clients experiences. The role drives scalable AI foundations, Search and Chat convergence, personalization, experimentation, reliability, security, and operational excellence. It involves leading cross-organizational initiatives, partnering with product, design, data science, and machine learning teams, mentoring senior engineers, and shaping multi-year technical roadmaps for customer-facing applications.