Top Senior Engineer Jobs in Seattle, WA

Own and evolve core transaction platform services handling global remittances at scale. Responsibilities include designing resilient APIs and distributed systems, improving delivery estimates, leading multi-engineer initiatives, migrating production systems, managing reliability and on-call operations, evaluating AI-generated code, collaborating cross-functionally, and mentoring engineers.

Build and maintain scalable GTM data pipelines, marts, semantic models, and dashboards supporting headcount planning, quota attainment, pipeline, and sales effectiveness. Partner with GTM analysts and stakeholders to establish reliable metrics, enable data tools, document systems, and develop AI-powered natural-language data querying capabilities. Translate complex data concepts for executives and engineers while ensuring high-quality, maintainable data products.

Design, implement, maintain, and troubleshoot large-scale global backbone and data center networks. Automate network provisioning and compliance using Python, Golang, and Salt; develop scalable routing and connectivity solutions; conduct capacity planning; optimize performance and availability; lead architecture reviews, maintenance windows, incident response, and network deployments; and provide technical guidance and documentation.

Build AI-powered applications for enterprise customers using Generative AI, LLMs, MongoDB, and modern software architectures. Responsibilities include refactoring applications, migrating legacy databases, optimizing performance, developing microservices and intelligent automation, supporting AI adoption, and influencing product strategy. The role collaborates with engineering, product, and customer teams to deliver scalable solutions and requires substantial enterprise software engineering experience.

Designs and maintains scalable data pipelines and infrastructure, establishes data governance and quality standards, and drives technical decisions across Headway’s data platform. Partners with Product, Engineering, Analytics, Data Science, Infrastructure, Finance, and Insurance Systems to improve architecture, performance, reliability, security, and AI-readiness. Ensures data is complete, traceable, trustworthy, and suitable for powering internal tools and AI-driven insights.

Build and operate scalable observability platforms covering metrics, logs, traces, alerting, dashboards, and service health. Develop self-service tooling, improve incident detection and response, establish observability standards, and optimize reliability, performance, and cost across global cloud-native systems. Partner with engineering, infrastructure, SRE, security, and product teams while participating in design reviews, incident response, and post-incident learning. Mentor engineers and promote operational excellence.

Leads the development and deployment of software and data solutions using Palantir Foundry and AI/ML technologies. Responsibilities include data engineering, analytics, system improvement, client account management, project ownership, team leadership, coaching, stakeholder engagement, and continuous improvement. The role requires building scalable applications with large datasets and delivering customer-focused solutions.

Own and improve CoreWeave’s vulnerability management program across applications, cloud infrastructure, and hardware. Build production security automation, triage and remediation workflows, prioritization models, metrics, and risk reporting. Investigate hardware-adjacent vulnerabilities, support zero-day response, conduct root-cause analysis, and collaborate with engineering and operational stakeholders. Contribute to architecture decisions, technical standards, code reviews, documentation, and teammate onboarding.

As a Software Developer in Test, you will validate cloud-side software, enhance automated test coverage, and improve product quality in a collaborative team.

Develop and qualify inertial navigation systems across the product lifecycle. Responsibilities include sensor calibration, performance evaluation, simulations, numerical data analysis, systems integration, verification, trade studies, FMEA, root-cause analysis, requirements development, technical documentation, design reviews, customer communication, and cross-functional engineering collaboration. Provide technical guidance to junior team members and support proposals, certification, and aerospace program delivery.

Build and operate DigitalOcean’s managed agent runtime platform for secure, scalable AI coding agents and agentic workflows. Responsibilities include distributed services, agent framework integrations, control-plane APIs, execution orchestration, sandboxed environments, developer APIs, observability, reliability, and end-to-end feature ownership. The role requires extensive backend engineering experience, AI/LLM integration expertise, distributed systems knowledge, and experience with cloud infrastructure, orchestration, databases, and high-throughput APIs.

Design, develop, and maintain large-scale data infrastructure and tooling, including test infrastructure, data frameworks, compute clusters, metadata catalogs, and governance systems. Partner with product managers and engineers across teams to achieve product goals. The role requires strong software development experience, proficiency in Java or Go, and expertise in distributed systems.

Provides senior 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, model correlation, flight-test validation, and design reviews. Drives optimization across performance, weight, cost, and manufacturability, partners with cross-functional aircraft teams, mentors structural analysts, and advances internal analysis standards.

Design and implement application security controls across cloud environments, assess cloud architectures for vulnerabilities and identity risks, conduct security reviews and penetration testing, monitor security events, support incident response, and develop cloud security requirements. Translate technical findings into remediation recommendations, interpret risk data, collaborate with clients, and mentor junior team members.

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.

Own the architecture, analysis, design, integration, testing, and flight validation of aircraft Environmental Control Systems. Perform thermal-fluid trade studies, system sizing, modeling, CAD design, supplier management, test planning, instrumentation, flight-data analysis, troubleshooting, and rapid hardware iteration for a special access aerospace program.

Leads mechanical integration of weapons and payloads onto aircraft, including weapons bays, external pylons, fittings, suspension and release fixtures. Collaborates with aircraft design and analysis teams, supports design reviews, and contributes to ground and flight testing. Serves as the subject-matter expert for physical weapons integration on the X-BAT platform.

Own the architecture and technical direction of a high-volume recognition platform spanning edge ingestion, identity resolution, real-time decisioning, notifications, and operator tools. Lead streaming pipeline design, privacy and consent models, observability, reliability, testing, progressive delivery, RFCs, cross-functional alignment, and mentorship. Build systems that combine deterministic rules with probabilistic computer vision signals under physical-world deployment constraints.

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.

Design, develop, deploy, monitor, and maintain AI-enabled enterprise and cloud security solutions. Build machine learning workflows, data pipelines, and scalable security capabilities using Python, Java, TensorFlow, and related libraries. Analyze security risks and data quality, translate technical findings into client recommendations, support security controls across cloud and infrastructure environments, review deliverables, and mentor junior team members.

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.

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.

Leads end-to-end delivery of agentic AI systems embedded within business units. Designs LLM applications, RAG pipelines, prompt orchestration, multi-agent workflows, integrations, and data pipelines for production environments. Owns architecture, experimentation, ROI measurement, data quality, stakeholder communication, and domain reporting. Mentors deployed engineers, establishes reusable engineering patterns, supports responsible AI and data security, and guides solutions from prototyping through production hardening.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Build and operate enterprise AI integration products, including MCP servers, agent-facing APIs, React and TypeScript experiences, semantic retrieval systems, and automated AI quality evaluations. Improve latency, reliability, token efficiency, observability, and security across distributed systems. Lead cross-functional engineering initiatives, mentor engineers, and implement enterprise safeguards such as tenant isolation, audit logging, OAuth, and prompt-injection defenses.