Top Senior Engineer Jobs in Seattle, WA

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.

Provides organization-wide technical leadership for Atlassian’s Trusted Data Platform and Data Exchange Platform. Defines architecture and multi-year strategy for cloud-agnostic distributed data infrastructure, streaming, compute, platform intelligence, reliability, and disaster recovery. Leads cross-team initiatives, resolves architectural ambiguity, establishes engineering standards, drives reusable platform capabilities, manages incident response, and mentors senior and principal engineers across distributed global teams.

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.

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.

Build and operate scalable machine learning systems for Atlassian’s Growth organization, including personalized recommendations, ranking, decisioning, experimentation, and customer journey optimization. Partner with product, engineering, data science, analytics, marketing, and sales teams to translate growth opportunities into measurable production capabilities. Develop datasets, features, models, evaluation frameworks, and monitoring systems while optimizing for customer value, fairness, quality, and long-term business outcomes.

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.

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.

Set technical vision and build AI foundations used across Atlassian: prototype and write code, build scalable AI infrastructure, measure AI effectiveness, provide context and cost-efficient compute, partner with product teams, and mentor senior engineers.

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.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Design, build, deploy, and operate scalable backend services and platform capabilities for growth engineering. Develop distributed systems, APIs, event-driven architectures, real-time processing, AI-powered workflows, and intelligent decision systems. Own end-to-end technical delivery while ensuring reliability, security, observability, and performance. Collaborate with Product, Data Science, Marketing, Sales, and Engineering teams, solve ambiguous business problems, improve engineering quality, and mentor other engineers.

Build and own scalable, high-performance frontend experiences for growth, including websites, funnels, campaign platforms, personalized journeys, and AI-powered tools. Lead end-to-end delivery, optimize performance and conversion through experimentation, and collaborate with Product, Design, Marketing, Data Science, and Backend Engineering. Improve frontend architecture, reusable components, accessibility, SEO, and engineering quality while mentoring teammates and solving ambiguous business problems.

Owns the design and qualification of flight-control actuation systems for a Group 5 UAV fighter aircraft. Responsibilities include requirements ownership, actuator sizing and dynamic analysis, control-law integration, power, thermal and structural integration, supplier execution, qualification testing, hardware delivery, and technical reviews through SRR, PDR, and CDR.

Owns the architecture, requirements, integration, verification, and fielding of weapons mission systems for autonomous aircraft. Leads trade studies across mission computing, avionics, payloads, communications, storage, hardware, and software. Drives laboratory, hardware-in-the-loop, flight testing, safety, airworthiness, and compliance activities. Serves as technical authority, leads multidisciplinary reviews, resolves integration risks, and mentors engineering teams. The role requires extensive aerospace or defense experience, weapons integration expertise, and an active U.S. Secret clearance.

Design and develop complex mechanical and actuated systems for autonomous UAVs, including CAD, structural and kinematic analysis, FEA, tolerance analysis, prototyping, testing, and production release. Collaborate across engineering teams, support aircraft integration and flight testing, ensure reliability and performance goals, mentor junior engineers, and provide technical direction.

Lead aircraft reliability engineering across development, integration, testing, and operations. Establish reliability requirements and budgets, perform predictions and analyses including FMEA, FTA, RBD, and Weibull analysis, manage FRACAS and reliability growth activities, analyze test and field data, assess supplier performance, and recommend design improvements. Communicate reliability risks and metrics to engineering leadership and customers while mentoring junior engineers and improving organizational processes.

Performs structural analysis and testing for advanced UAV airframes and components. Builds finite element models, conducts static, modal, fatigue, vibration, and shock analyses, validates results through hand calculations, and analyzes telemetry and test data. Collaborates with engineering teams to optimize lightweight composite and metallic structures for strength, weight, safety, and manufacturability. Documents technical findings and supports structural test planning, certification, and continuous improvement of analysis methods.

Leads the design and development of semantic data models, governed metrics, and agentic AI solutions for healthcare use cases. Builds LLM applications using RAG, vector search, prompt engineering, and Snowflake Cortex. Establishes AI evaluation, guardrails, observability, and cost optimization practices while mentoring engineers, shaping platform strategy, and partnering across analytics, product, governance, QA, and business teams.

Provides technical leadership for structural analysis and validation of UAV airframes and components. Leads finite element modeling, static, modal, fatigue, vibration, and damage-tolerance analyses; supports structural testing, flight-test validation, and analysis-to-test correlation. Drives structural optimization, reviews technical deliverables, mentors analysts, and collaborates with multidisciplinary engineering teams to balance aircraft performance, weight, cost, and manufacturability.

Performs offensive product security assessments, penetration testing, vulnerability research, and exploit validation across embedded vehicle software, mobile applications, backend systems, and product services. Identifies attack paths and vulnerabilities, validates impact, provides risk-ranked remediation guidance, and partners with engineering teams to improve secure SDLC practices. The role also develops offensive tooling and testing methods.

Lead architecture, requirements, behavior modeling, safety analysis, CONOPs, integration, and testing for a launch and recovery vehicle supporting autonomous UAS operations. Integrate avionics, hydraulics, power, navigation, mechanical, electrical, and data interfaces while coordinating cross-functional teams, DoD stakeholders, and industry partners. Define transport, deployment, field operations, and sustainment concepts for a mission-ready system.

Lead avionics architecture, requirements, system behavior, safety, interface management, navigation, communications, and hardware/software integration for autonomous UAS platforms. Define MBSE models, conduct trade studies, integrate avionics data buses and sensor systems, support flight testing and validation, and ensure compliance with military and aviation standards. Collaborate with autonomy, software, hardware, manufacturing, flight operations, suppliers, and DoD stakeholders from clean-sheet development through first flight and deployment.

Designs and operates distributed database systems across query execution, transaction processing, durable storage, concurrency, replication, recovery, and upgrades. Responsibilities include improving database performance, building deterministic failure simulations, managing stateful rollouts, participating in on-call response, leading production changes and investigations, and mentoring engineers. The role requires deep systems expertise and experience operating correctness-sensitive stateful services in production.