Top Staff Software Engineer Jobs in San Francisco, CA

Design, build, and own high-throughput backend and infrastructure services for a healthcare AI agent platform. Ensure reliability, graceful degradation, observability, performance, and secure handling of sensitive patient data. Drive architecture and operate services end-to-end in production.

Own the end-to-end PEO onboarding experience, from discovery through setup. Set technical direction, design durable full-stack solutions, integrate systems across teams, and improve conversion through funnel instrumentation and experimentation. Build in a regulated domain with strong quality, testing, and observability standards. Use AI tools in engineering workflows and develop AI-assisted customer guidance. Influence cross-functional partners and raise engineering standards across the team.

Lead the technical vision, architecture, and evolution of Uber’s large-scale streaming data platform. Design resilient distributed systems, optimize performance and resource utilization, improve reliability and developer experience, and drive modernization across teams. Provide technical leadership for complex initiatives, establish engineering standards, mentor senior engineers, and shape long-term infrastructure strategy for high-throughput, low-latency, multi-tenant workloads.

Lead backend engineering for real-time defenses against bots, abuse, and fraud. Design rate limiters, challenge flows, rules engines, enforcement APIs, telemetry, and monitoring across edge and product services. Protect signup, login, payments, and song generation from account farming, credential stuffing, card testing, and credit abuse. Shape the team’s architecture, roadmap, on-call practices, and engineering standards.

Lead full-stack engineering projects across Harvey’s AI-powered legal products. Build scalable document ingestion, data collection, secure systems, AI-native features, agentic orchestration, retrieval systems, and integrations across web, desktop, mobile, Google Docs, MS Word, and APIs. Develop intuitive frontend experiences with React and TypeScript and robust backend services with Python and PostgreSQL, supporting streaming results, long-running processes, and complex tool-calling workflows.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

Own the architecture, service contracts, reliability, and production operation of Trustly’s Identity and Verification services. Responsibilities include on-call support, incident response, observability, documentation, AI-assisted implementation, integration testing, cross-region knowledge transfer, and hiring and leading the future team. The role requires strong distributed-systems expertise and experience with authentication or identity verification in regulated financial products.

Define and evolve Aurora’s autonomous-vehicle runtime framework across on-vehicle and cloud execution. Design high-performance, low-latency IPC and RPC, manage configuration and process lifecycles, improve deterministic simulation and log replay, and build fault monitoring and recovery capabilities. The role also involves creating intuitive C++ APIs, supporting field testing, mentoring engineers, setting coding standards, and driving the framework roadmap.

Lead the design and implementation of robotics software spanning motion planning, real-time control, perception, optimization, tooling, embedded systems, cloud infrastructure, and robot operations. Own complex software architecture, influence technical direction, mentor engineers, lead technical projects, and establish engineering best practices across the robotics stack. Collaborate across hardware, software, and deployment teams to scale production robotics systems.

Lead architecture and implementation of core software platforms spanning backend services, infrastructure, applications, machine learning systems, data pipelines, internal tools, and customer-facing products. Own complex systems, establish technical direction, make architectural tradeoffs, and build scalable, reliable solutions. Collaborate with robotics, hardware, operations, and other teams to improve company effectiveness at scale.

Architect and build Traba’s AI agent platform, including orchestration, model strategy, retrieval, memory, tools, evaluation, observability, and integrations with WMS, TMS, ERP, and customer systems. Lead foundational technical decisions, spend time with customers to shape deployments, establish quality and reliability standards, hire and mentor engineers, and partner with company leadership on the platform roadmap.

Lead the architecture, scalability, reliability, performance, and cost efficiency of Cisco’s data platform. Define multi-quarter technical direction for storage, streaming, compute, observability, and performance tooling. Establish standards for schemas, migrations, SLOs, tenancy, retention, and capacity planning; lead major distributed-systems initiatives and production investigations; make technology and cost decisions; and mentor engineers across teams.

Lead end-to-end development of reliable care-agent tooling and software services integrating contact-center platforms with backend systems. Own architecture, performance, security, production operations, and technical direction while partnering with product, care operations, QA, and engineering teams. Translate operational workflows into scalable solutions, mentor engineers, and improve tools using agent feedback, operational metrics, and AI-assisted development practices.

Lead technical vision and strategy for Affirm’s Cards backend engineering organization. Architect scalable and reliable platforms, guide cross-team initiatives, champion AI adoption and engineering excellence, influence technical roadmaps, and mentor engineers through Staff level. The role requires strong architectural expertise, organizational leadership, project management, communication, and cross-functional collaboration.

Lead technical execution and architecture for the Loyalty product. Own roadmap, design distributed services and APIs, ensure reliability/observability, integrate with ZMP and AthenaOS, build and coach engineering teams, and drive cross-team delivery of scalable multi-tenant loyalty capabilities.

Set technical direction for Toast’s Developer Experience platform, including local development, build tooling, shared libraries, testing frameworks, and developer portals. The role combines hands-on software engineering with architecture, cross-team technical leadership, AI-assisted development, developer productivity measurement, mentorship, and roadmap prioritization.

Serve as the senior hands-on technical lead for a small product engineering team. Build and own customer-facing AI-driven features across the full stack, including interfaces, backend services, infrastructure, and data pipelines. Mentor junior engineers, establish code review and engineering practices, guide architecture, and collaborate with product, customer-facing teams, and leadership to define priorities. The role requires recent production experience with language-model functionality and may involve AI evaluation, LLM observability, and agentic pipelines.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

Lead architecture and development of high-scale segmentation systems, data pipelines, APIs, and dashboard experiences. Own backend systems end to end, improve performance and reliability, influence product direction, mentor engineers, and drive operational excellence. Work with massive data volumes and complex real-time workloads while collaborating across engineering, product, and stakeholder teams.

Lead architecture, development, reliability, and improvement of foundational data platforms covering storage, streaming, caching, indexing, and warehousing. Collaborate with cross-functional stakeholders, deliver platform features, improve engineering practices and tooling, present technical guidance, and mentor engineers. The role requires extensive distributed systems, cloud, database, containerization, deployment, and large-scale data platform experience, with emphasis on technical leadership and organizational collaboration.

Build and own backend services and APIs supporting money movement, payments, settlements, reconciliation, payroll integrations, retirement workflows, and financial compliance. Ship reliable production code, debug incidents, improve observability and operational tooling, and collaborate with product, compliance, operations, and engineering teams. Mentor engineers and ensure systems handling customer funds are correct, scalable, and well tested.

Lead the architecture and development of secure, scalable GenAI platforms, including LLM applications, agentic workflows, RAG systems, evaluation frameworks, observability, multi-tenant infrastructure, security controls, developer APIs, and deployment tooling. Optimize model performance, cost, latency, and reliability while integrating providers and frameworks such as OpenAI, Bedrock, MCP, and LangGraph. Develop software using C#, Java, JavaScript, Go, or Python.

Provides technical leadership for scalable Kubernetes-based infrastructure across multi-cloud and hybrid environments. Architects control plane services, tenancy models, deployment pipelines, observability, and reliability capabilities. Drives modernization through GitOps, policy-as-code, networking, autoscaling, CI/CD, and container lifecycle practices. Collaborates with SRE, platform, and application teams, mentors engineers, influences technical roadmaps, participates in on-call rotations, and improves infrastructure performance, resilience, and developer experience.

Design and build Uber’s large-scale machine learning training infrastructure, addressing challenges involving massive datasets, models, distributed systems, and GPU resources. Mentor engineers, establish technical standards, review architectures and code, optimize performance and utilization through statistical analysis, and develop tools that improve engineering velocity. Lead complex projects from inception through global productionization while coordinating with Product, Data Science, and Operations teams.

Leads the architecture and delivery of GitLab’s static analysis engine and evaluation tooling. Owns program modeling, vulnerability detection quality, SAST rules, AI-assisted development guardrails, and benchmark validation. Sets technical direction, mentors engineers, coordinates complex initiatives, contributes to research and open source, and participates in on-call rotations. The role requires deep program analysis, application security, systems programming, performance optimization, containerized CI/CD, and experience building trustworthy LLM tooling.