Software Engineer Jobs in Sunnyvale, CA

Design, develop, verify, and maintain embedded system software and kernel drivers for NVIDIA Tegra ARM-based SoCs. Responsibilities include pre-silicon modeling, silicon bring-up, core OS enablement, hardware and software collaboration, unit testing, documentation, and system-level debugging. The engineer will also influence hardware architecture through design specifications and support next-generation SoC platforms.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Design and implement cloud-native data management services (catalog, metadata, dataset/checkpoint lifecycle) for exabyte-scale GPU workloads. Build backend systems on Kubernetes and major cloud providers, collaborate with research and cross-functional teams, document architecture, and drive integration with storage/compute innovations (GPU Direct Storage, DPU).

Build and ship full-stack, AI-native products from prototype through production. Design agentic systems involving model interactions, tools, retrieval, orchestration, evaluations, observability, and guardrails. Make architecture decisions, own reliability and security, collaborate with product and design teams, and use AI development tools to accelerate delivery while maintaining quality. Help establish engineering standards and practices for small, autonomous product teams.

Develops autonomous-driving simulation technology at Waymo. Responsibilities include analyzing simulator performance, identifying gaps, documenting findings, collaborating with stakeholders, and developing simulator functionality through clean APIs. The internship targets master's and PhD students with software engineering, evaluation, and API design experience, preferably involving autonomous simulation, distributed computing, data pipelines, rendering engines, and exploratory research.

Leads platform engineering across multiple teams by architecting scalable, cloud-native infrastructure, reusable tooling, distributed systems, and AI/ML-enabled applications. Drives the full software development lifecycle, including CI/CD, testing, monitoring, incident response, and reliability engineering. Provides technical leadership and mentorship, establishes architectural standards, translates business needs into technical solutions, and advances continuous improvement, scalability, maintainability, and operational excellence.

Leads development of scalable platform capabilities across the full software lifecycle, including design, coding, testing, deployment, monitoring, and maintenance. Applies cloud-native architecture, microservices, containers, automation, and AI/ML components to build resilient systems. Conducts design reviews, mentors engineers, influences technical decisions, collaborates across teams, and promotes engineering excellence through documentation, metrics, and continuous improvement.

Leads platform engineering initiatives for Walmart’s Unified Recommendations Platform. Designs scalable cloud-native infrastructure, reusable tooling, distributed systems, data pipelines, and AI-enhanced applications. Oversees the full software development lifecycle, including coding, testing, deployment, maintenance, and incident response. Provides architectural direction, mentors engineering teams, collaborates with stakeholders, and drives reliability, performance, operational excellence, and continuous improvement for high-volume recommendation systems.

Leads development of scalable, cloud-native platform capabilities across the full software lifecycle. Designs resilient systems, integrates AI and ML components, conducts architecture reviews, improves reliability and developer productivity, and mentors engineers. The role involves coding, testing, deployment, monitoring, stakeholder collaboration, roadmap execution, and continuous process improvement.

Develop and optimize Windows display drivers and graphics subsystems for Horizon virtual desktops. Responsibilities include driver feature ownership, Direct3D and GPU optimization, display topology and EDID management, kernel and user-mode debugging, VNC integration, crash analysis, testing, driver deployment, performance tuning, technical documentation, mentoring, and cross-functional resolution of customer-facing display issues.

Design, develop, test, and optimize system software for next-generation ARM-based SoCs across pre- and post-silicon phases. Analyze architectural bottlenecks, workload performance, and performance-per-watt tradeoffs; develop hardware/software policies and optimization strategies. Lead performance analysis, competitive benchmarking, architecture documentation, and adoption of improvements across NVIDIA software products and open-source SDKs.

Build and maintain scalable full-stack integrations between Commure and healthcare systems, including EHRs and insurance providers. Design end-to-end workflows, use LLMs to reverse-engineer EHRs, improve architecture and reliability, troubleshoot production issues, implement monitoring, and ensure HIPAA, HL7, and FHIR compliance. Collaborate with product, healthcare, DevOps, QA, and security teams throughout development and deployment.

Build and ship full-stack applications, backend services, APIs, fleet-monitoring tools, routing systems, and simulation infrastructure for autonomous vehicles. Product Engineers own features end-to-end, collaborate with Product, UX, Operations, and engineering teams, and contribute to scalable software across frontend, backend, cloud, and onboard vehicle systems.

Build and operate NVIDIA’s serverless AI inference platform across globally distributed, GPU-backed Kubernetes clusters. Lead feature implementation, testing, performance optimization, reliability improvements, customer issue resolution, reference architecture development, and technology evaluation. Mentor engineering teams on scalable AI/ML workloads and contribute to product requirements and roadmap development.

Build scalable distributed systems and ML infrastructure for Neural Simulation, including data pipelines, simulation workflows, storage, model training, evaluation, inference, and observability. Develop backend services that transform real-world driving data into high-fidelity simulation environments and labeled datasets. Collaborate with infrastructure, autonomy, research, and product teams while owning features from design through deployment.

Design and own geometric map-generation algorithms that process large-scale lidar datasets into automated 3D maps for autonomous vehicles. Responsibilities include sensor optimization, pose-graph optimization, multimodal alignment, pipeline scalability, failure-mode analysis, algorithmic debugging, data-quality improvement, and integration with neighboring teams. The role requires strong C++ development, 3D geometry, point-cloud processing, linear algebra, numerical optimization, and probability expertise.

Build performance engineering tools, data pipelines, and processing systems for onboard and simulated autonomous-vehicle software. Collect and analyze large-scale performance data, improve software latency and hardware utilization, diagnose performance issues, and support visualization and analysis workflows. Collaborate with autonomy, sensing, and vehicle platform infrastructure teams on optimization efforts.

Develop software for data storage and management systems, contributing across the software development lifecycle from design through testing. Work on data efficiency, deduplication, reclamation, replication, performance, scalability, and reliability. Collaborate with senior engineers and cross-functional teams while writing efficient C++ code and maintaining automated test coverage. This primarily in-office role is based in Santa Clara, California.

Software Engineer Interns will own projects from requirements and technical design through implementation, testing, and delivery. They will develop features, optimizations, and infrastructure tools for a data storage and cloud platform; write unit and integration tests; participate in design discussions and code reviews; and collaborate with mentors and cross-functional teams. This is an in-office role based in Santa Clara, California, requiring full-time availability for 12 consecutive weeks during Summer 2027.

Design and operate distributed userspace infrastructure for long-running engineering workflows on bare-metal Linux. Build coordination over NFS, orchestration around IBM LSF, reliability and performance tooling, and incremental modernization from legacy languages such as Perl to Go. The role involves managing concurrency, retries, observability, staged rollouts, and production systems at scale while collaborating with engineers to create durable workflow platforms.

Lead and grow the Admin Console engineering team while owning architecture and execution for enterprise workflows involving data sources, security, deployment, governance, permissions, and AI configuration. Partner with Product, Design, infrastructure, backend, connector, and security teams to simplify complex workflows, improve self-serve activation, increase deployment visibility, and build scalable control-plane capabilities for Glean customers.

Lead architecture and hands-on development of reusable customer-facing AI platform services, including conversational AI, agent orchestration, retrieval, grounding, memory, tool integration, and policy enforcement. Define APIs, data models, deployment designs, testing, evaluation, and operational requirements. Build production systems across GCP and Azure while driving reliability, security, observability, CI/CD, and technical standards. Provide staff-level influence through architecture reviews, mentoring, code reviews, and cross-functional leadership without direct reports.

Build and operate database and storage platform software, including services, APIs, developer tools, migrations, and lifecycle automation. Solve database scaling, performance, reliability, and availability problems across distributed systems, cloud infrastructure, and backend services. Partner with engineering teams on schemas and queries, lead technical projects, modernize platforms, participate in incident response, and use AI-assisted tools in development.

Build and operate database and storage platform software, including services, APIs, and developer tools for provisioning, migrations, upgrades, and lifecycle management. Solve scaling, performance, reliability, and data correctness challenges across databases, distributed systems, cloud infrastructure, and networking. Partner with engineering teams on schemas and platform adoption, lead technical projects through rollout, participate in on-call response, and use AI-assisted tools in development.

Build and deploy AI-powered solutions for clients by scoping ambiguous problems, designing LLM workflows, integrating data and retrieval systems, and creating scalable production tooling. Collaborate with technical and non-technical stakeholders, iterate based on operator feedback, optimize workflows, and support customer deployments. The role requires strong Python and software or ML engineering skills, production deployment experience, communication abilities, customer on-call availability, travel as needed, and hybrid office attendance.