Software Engineer Jobs in Sunnyvale, CA

As a Staff Software Engineer on the Inference team, you'll lead architecture and optimization for Kubernetes-native AI workloads, enhancing performance and reliability across services.

Lead design and implementation of multi-regional data platform and stream-processing architecture. Drive event-driven adoption, mentor engineers, ensure scalability, reliability, security, compliance, and participate in on-call operations.

The Staff Software Engineer will lead technical projects on the Secondary Driving System, focusing on software architecture, multi-sensor perception, and integration, while mentoring the engineering team.

Build and lead backend engineering for a cloud provisioning platform that automates customer onboarding and configuration. Design and scale distributed systems, mentor engineers, prototype to production, collaborate cross-team, improve architecture, and deliver high-quality, tested code for large-scale, high-throughput environments.

Lead design and implementation of high-scale cloud backend authentication/authorization services. Build and operate event-driven microservices, mentor engineers, improve architecture and reliability, and contribute to open source. Focus on AuthN/AuthZ, fine-grained access control, scalability, and production ownership.

Architect and scale Walmart’s mobile cart and checkout experiences using Node.js and GraphQL. Lead technical design, implementation, distributed-system integrations, production reliability, code reviews, and cross-functional transaction-flow delivery. Mentor engineers and promote secure, performant, maintainable engineering practices while integrating backend REST and GraphQL APIs and exploring AI/ML capabilities.

Develop, implement, integrate, and verify motion control systems for autonomous vehicle platforms. Responsibilities include creating safety-critical motion control algorithms, analyzing performance in offline and on-road environments, and validating robustness against disturbances, failure modes, and environmental conditions.

Design, develop, and maintain scalable backend infrastructure and data-processing pipelines for Aurora’s static and dynamic mapping systems. Build reliable services for storing and serving map data, improve testing and performance, and collaborate with internal and external customers on system designs and features. The role focuses on distributed systems, data versioning, cloud-native technologies, and high-throughput mapping infrastructure supporting autonomous vehicles.

Develops real-time sensor-data algorithms and owns the design, implementation, testing, deployment, and maintenance of online calibration software for autonomous vehicles. Collaborates with software, hardware, manufacturing, and operations teams to keep vehicle sensors accurately aligned for safety-critical autonomy functions.

Build and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and data platform tools. Develop cloud-based infrastructure using AWS and Terraform, optimize processing for reliability and cost, and implement monitoring, logging, governance, quality, and security practices. Collaborate with data scientists, analysts, autonomy engineers, and product teams to deliver reliable data solutions.

Develop real-time behavior-planning algorithms for autonomous vehicles, selecting safe and comfortable trajectories. Integrate motion planning with machine learning, build production-grade Aurora Driver software, and create metrics and testing workflows for algorithm iteration. The role requires strong Python or C++ programming experience in low-latency or real-time environments and experience productionizing motion-planning algorithms. GPU and machine-learning toolkit expertise is preferred.

Leads the design, development, deployment, and maintenance of scalable platform capabilities, infrastructure, tooling, AI/ML frameworks, and iOS advertising solutions. Builds reusable iOS frameworks and SDKs, applies cloud-native architecture, conducts system design reviews, integrates machine learning, and drives performance and reliability. Mentors engineers, influences technical decisions, collaborates cross-functionally, and uses experimentation, analytics, and data-driven practices to improve developer productivity and consumer applications.

Design and implement foundational data systems for AI, focusing on efficiency and performance at scale. Collaborate on systems that optimize data handling and contribute to research advancements.

Lead technical strategy and architecture for Developer Experience: build scalable build/test/delivery systems, improve CI/CD throughput, design internal platforms/APIs/SDKs/CLIs, integrate LLM/agentic tooling safely, mentor senior engineers, and drive cross-team initiatives to increase engineering velocity, reliability, and maintainability at organizational scale.

Architect and build AI-native, large-scale distributed systems for ITSM/ITOM products. Lead technical strategy, prototype and validate architectures, drive evidence-based experiments, establish patterns and best practices, mentor senior engineers, and collaborate across teams to deliver reliable, observable, and cost-effective agentic automation and operational intelligence.

Lead technical strategy and architecture for CoreWeave's orchestration platform (SUNK). Own critical orchestration components, drive cross-org initiatives in scheduling, quota enforcement, and hyperscale scaling. Mentor senior engineers, establish reliability and observability best practices, and ensure workloads run reliably and efficiently for large GPU ML training and inference clusters.

Join a small Applied Training team building Kubernetes-native research cluster and sandbox infrastructure for large-scale ML training. Design and implement cluster experiences (CLI, job schemas, operators), own Python SDKs for RL rollouts, work directly with customers and infra teams, document OSS training frameworks, and ship production-grade ML infrastructure solving distribution, scheduling, and isolation at scale.

Design, develop, and operate scalable production agentic systems and UI components. Own features end-to-end, integrate frontier AI SDKs, implement RAG/retrieval patterns, build testing and observability, mentor engineers, and raise engineering quality for AI-powered enterprise products.

Lead design, develop, and operate containerized Java and Python microservices on Kubernetes/OpenShift. Build scalable APIs, asynchronous and event-driven systems, integrate frontier AI SDKs, ensure reliability and observability, and provide technical leadership through architecture and code reviews while partnering with product, research, security, and infrastructure teams.

Take end-to-end ownership of a Kubernetes-based, multi-region data platform: architect for high availability and low latency, scale infrastructure, optimize CI/CD, implement observability and IaC, enforce cloud-native security, lead incident response, and ensure production readiness and capacity planning.

Design, build, and operate high-throughput, fault-tolerant ledger and settlement systems of record. Develop durable ledgering APIs, settlement orchestration, and treasury automation. Ensure data contract integrity for downstream finance, run production systems to SLOs, debug multi-layer issues, lead and mentor engineers, and use AI tooling to reduce operational toil.

Design and implement data platforms, manage data infrastructure, develop streaming architectures, improve performance, and ensure data compliance.

Lead design and implementation of high-performance, scalable integrations that ingest identity data from cloud and SaaS providers. Drive architecture and best practices, mentor backend engineers, partner with product and customer teams, and apply AI/ML models to streamline connector development and data modeling for Veza's Access Graph platform.

Build, scale, and operate Moveworks AI cloud infrastructure and developer platform. Design secure, resilient multi-region/cloud systems; create IaC-driven CI/CD pipelines; manage Kubernetes/Istio clusters; collaborate with platform, ML, data, frontend, and security teams to meet performance, observability, and cost objectives.

Build and own distributed runtime infrastructure for agentic AI: session orchestration, lease-based distributed session management, event-driven pipelines, structured concurrency, observability, caching and state layers to support low-latency, correct, long-running agent workflows at scale.