Top Principal Software Engineer Jobs in San Francisco, CA

Own the company-wide cloud networking strategy and architecture across AWS, multi-region and multi-cluster environments. Lead network automation, AI-enabled infrastructure operations, progressive delivery, observability, security standards, and major incident response. Partner with engineering, security, compliance, SRE, and executives to deliver resilient, scalable networking. Mentor senior technical staff and guide multi-year, cross-functional initiatives.

Principal Software Engineer responsible for owning customer-facing products end to end, leading technical strategy, architecting large-scale distributed systems, and delivering AI-native experiences. The role involves full-stack implementation, product strategy, system design, code reviews, reliability, testing, telemetry, security, performance, and mentoring engineers. Candidates must have extensive experience building and operating large-scale systems and strong product, UX, communication, and collaboration skills.

Provides hands-on technical leadership for enterprise Data Trust platforms. Designs and operates distributed systems supporting data contracts, metadata, lineage, quality, privacy, access controls, reliability, and semantic context for analytics and AI. Leads batch and streaming capabilities across Snowflake, AWS, and Kafka; establishes observability, SLOs, automated enforcement, and operational rigor. Drives cross-functional architecture and multi-quarter initiatives while mentoring engineers and building reusable platform standards.

Owns company-critical, long-term technical problems across multiple teams and organizations. Defines technical strategy, architectures, roadmaps, and engineering standards for large-scale customer-facing systems. Remains hands-on in software development while driving AI adoption in products and engineering workflows. Partners with senior product, engineering, and executive leaders to resolve complex problems, improve reliability and performance, and shape Dropbox’s technical direction.

Lead architecture and hands-on development for cross-product onboarding and growth initiatives spanning Atlassian products. Build React and TypeScript micro-frontends, backend microservices, APIs, experimentation frameworks, and telemetry systems. Drive performance, reliability, modernization, and operational excellence while collaborating across product teams. Mentor senior engineers, author technical specifications, lead design reviews, and establish reusable patterns for multi-product user journeys.

Lead the mobile engineering team in evolving iOS and Android applications toward an AI-native, thin-client architecture. Define participant API and platform direction, contribute Python to backend services, establish AI-assisted development processes, improve DevOps and continuous delivery, mentor engineers, and partner closely with Product and Design. The role requires principal-level technical leadership, product focus, system architecture expertise, and hands-on use of AI coding agents.

Lead the technical direction and implementation of large-scale SaaS product features. Build secure, reliable, maintainable, well-tested software; evaluate architectural trade-offs; debug team inefficiencies; and contribute to code reviews, documentation, and complex bug fixes. Collaborate across product, design, and engineering, influence strategy, identify organizational blockers, mentor engineers, and improve engineering practices around testing, performance, security, privacy, and reliability.

Leads architecture and hands-on development of WEX’s semantic data platform and Data-as-a-Service capabilities. Builds reusable semantic business objects, metadata and ontology frameworks, knowledge graphs, APIs, governance, lineage, quality, and data contracts. Solves distributed-systems and scalability challenges while enabling AI and agentic applications to use trusted enterprise data. Partners across engineering, product, analytics, risk, finance, and business teams; influences technical standards, drives adoption, mentors engineers, and remains involved in implementation.

Design and build large-scale, highly available Responsible AI cloud services. Lead architecture, development, release, and production readiness. Collaborate with engineers, scientists, and product managers to integrate Responsible AI features across Azure AI and related services, drive DevOps practices, and improve performance, observability, and reliability.

Lead the US Platform squad while remaining hands-on in production code. Own contracts across payments, bank connectivity, risk, and infrastructure; build complex cross-domain changes; maintain API specifications and contract tests; lead critical incident responses; and conduct load testing, failure drills, and capacity planning. The role requires deep distributed systems expertise, payment-platform experience, technical leadership, product judgment, and practical use of AI in regulated systems.

Sets technical vision for Pinterest’s ML and media infrastructure, leading distributed training, fine-tuning, evaluation, inference, GPU optimization, content ingestion, and signal-processing architectures. Partners with AI/ML, Ads, Content, Shopping, and GenAI teams to productionize reliable, scalable platforms, drive architecture decisions and migrations, improve operational standards, and mentor senior engineers.

Provides technical leadership for architecture and evolution of large-scale distributed data platforms supporting Azure network telemetry. Designs streaming and batch systems, establishes reusable engineering patterns, drives cross-team technical strategy, and owns capabilities from design through production operations. Ensures scalability, reliability, security, performance, data quality, and operational excellence while mentoring engineers and influencing technical leaders. The role also involves architectural reviews, dependency management, risk reduction, and applying AI or machine learning approaches to operational systems.

Leads architecture and production delivery of a near-real-time observability platform for large-scale AI supercomputers. Responsibilities include telemetry ingestion, processing, storage, querying, topology and health modeling, 1P/3P data convergence, AI-assisted anomaly detection, technician dashboards, repair validation, security, reliability, cross-team technical leadership, and mentoring.

Design and lead development of high-performance networking infrastructure for large-scale AI training and inference in Azure. Architect low-latency, low-jitter communication frameworks, optimize distributed systems, benchmark scalability, resolve complex networking issues, and improve reliability, observability, and performance. Mentor engineers, create technical designs, manage dependencies, and participate in on-call operations for critical services.

Leads engineering within a product domain, driving large-scale strategic initiatives across multiple teams. Establishes technical standards, architecture, and scalable best practices; aligns engineering, product, data science, and business stakeholders; guides roadmaps and resource allocation; communicates with executives; and coaches engineering leaders and senior individual contributors.

Build and own Arcadia’s internal developer platform across a large-scale data lakehouse and Kubernetes applications. Develop self-service APIs, templates, golden paths, CI/CD capabilities, observability, security controls, and reliability practices. Lead platform migrations, improve developer experience, manage stakeholder needs and costs, set technical direction, and coach senior engineers. The role requires hands-on software development alongside expertise in cloud infrastructure, Kubernetes, networking, infrastructure as code, SLOs, incident response, and platform product management.

Designs and develops secure, performant Copilot products and platform services across Windows, Mac, and mobile. Builds distributed AI and agent-driven systems capable of reasoning, planning, and executing tasks. Collaborates with infrastructure, platform, application engineering, and research teams to ship high-quality software. Champions AI-assisted development, coding-agent adoption, prompt design, tooling best practices, and human-in-the-loop systems.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

Lead architecture and hands-on development of Microsoft's AI Safety Platform, building observability, detection, investigation, threat-hunting, and distributed data-processing capabilities. Design attack detection infrastructure, correlate high-volume telemetry, apply AI security research, conduct security and privacy reviews, and partner across engineering, science, and incident response teams. Mentor engineers and provide technical leadership for complex AI security systems.

Lead architecture and hands-on development for Microsoft's virtualization platform, focusing on hypervisor and VMM capabilities. Design, implement, and optimize systems software across Windows, Linux, macOS, Azure, and containers. Improve performance, security, reliability, testing, and operational readiness; diagnose low-level hardware/software issues; enable virtualization on new silicon; mentor engineers; and influence platform strategy across teams and hardware partners.

Design and maintain scalable backend services, APIs, data-platform automation, and AI agent systems. Build Python and Go services on AWS, RAG pipelines, tool-calling agents, dbt and Airflow automation, and internal platform tooling. Implement identity, authorization, tenant isolation, observability, reliability, and infrastructure automation. Establish technical direction through architecture, code reviews, mentoring, and production support.

Build reliable autonomous AI agents for identity operations and security. Responsibilities include developing agent harnesses for planning, tool use, memory, recovery, model routing, authorization, evaluation, and long-running workflows. Integrate OpenAI and Anthropic APIs, measure quality and safety, create replay tests, investigate failures, protect tenant boundaries, and improve prompts, retrieval, tools, and models. The Principal Engineer will set product engineering direction and develop shared components for production AI systems.

Design, implement, and operate production-quality AI framework components, benchmark and optimize large model training and inference across GPUs and Microsoft hardware, build automation and observability, drive cross-team projects from definition to deployment, and provide technical leadership and mentorship for improvements in performance, reliability, and model onboarding velocity.

Build and improve Azure Kubernetes Service capabilities for Linux and Windows containers. Develop distributed cloud infrastructure, automation, observability tooling, and reliability improvements at global scale. Support AI-native infrastructure, including GPU orchestration, model deployment, inference, and platform operations. Accelerate engineering workflows, prevent production incidents, integrate AI tools, and contribute to Kubernetes and cloud-native platform innovation.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.