Software Engineer Jobs in San Jose, CA

Develop core decision-making and motion planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, path and velocity generation, trajectory optimization, world modeling, trajectory cost design, testing, debugging, and root-cause analysis. The engineer will collaborate with Prediction, Perception, and Control teams to deliver safe, comfortable, and intelligent vehicle motion in complex environments.

Build and maintain Python-based ETL pipelines, data models, integrations, and backend platform logic. Apply agentic AI workflows to infrastructure data for automation, correlation, and root cause analysis. Collaborate directly with customers to understand technical environments, implement integrations, support deployments, and translate feedback into engineering improvements. Work with cloud platforms, containers, SQL, complex data structures, and production systems while contributing to testing, automation, and customer enablement.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, AI agents, and orchestration frameworks. Build scalable AI services, models, APIs, and integrations with PLM, SAP, and other enterprise systems supporting engineering and manufacturing workflows. Collaborate with business and IT teams throughout the SDLC, improving solution quality through prompt engineering, evaluation, optimization, and production support.

Lead design and implementation of Switch Abstraction Interface (SAI) software for network switches, align SONiC models with SAI goals, drive architecture, optimize performance and serviceability, represent Cisco in the SAI open-source community, and mentor engineers while collaborating with hardware teams.

Design, build, and maintain backend services, platform infrastructure, and cloud deployments. Debug distributed production systems, architect IaC and networking, provision on-prem hardware, support customer deployments and integrations, and collaborate with research, product, and customer success to deliver reliable, scalable production software.

Build and maintain SRE microservices supporting a global GPU infrastructure platform. Implement GitOps, declarative configuration, and CI/CD automation; monitor metrics, logs, and traces; support operational readiness through on-call participation, runbooks, and incident reviews; and write unit, integration, and end-to-end tests. Work with senior engineers to deliver production-ready platform features while maintaining service reliability and infrastructure consistency.

Design, develop, and deploy scalable backend systems and RESTful services. Build microservices and distributed architectures, optimize performance and reliability, and manage cloud-based applications. Develop CI/CD automation, containerized deployments, database integrations, monitoring, and comprehensive testing strategies. Collaborate cross-functionally, participate in code reviews and design discussions, troubleshoot issues, and deliver reliable products aligned with business requirements and testing goals.

Build and ship production agentic systems for Roku’s streaming platform, including multi-step agents, internal tooling, real-time telemetry processing, distributed services, and hardware-in-the-loop workflows. Engineers own features across platform, application, UI, and cloud integration while working with AI tools on unfamiliar codebases. The role requires strong systems or infrastructure fundamentals and an existing shipped agentic project used by others.

Designs, develops, and maintains processor-generator infrastructure, build and delivery systems, database integrations, REST APIs, web applications, automated testing, and release processes. Builds Java and React applications, configures application servers, manages SQL and NoSQL databases, automates fulfillment, supports production operations, and collaborates across engineering, marketing, applications, and order-processing teams.

Develop Linux kernel and user-space power and thermal management software for NVIDIA Tegra and embedded platforms. Optimize performance, efficiency, latency, compute and memory utilization for edge AI and robotics workloads. Collaborate with hardware, software, and power architecture teams on platform estimation and optimization. Requires strong C, Linux, embedded systems, ARM, and system-level debugging experience.

Design, implement, and operate large-scale observability and telemetry platforms focused on performance, reliability, monitoring, logging, and alerting. Manage services throughout their lifecycle, including system design, deployment, capacity planning, automation, incident response, and postmortems. Build infrastructure tools and frameworks for private and public cloud systems, improve availability and latency, and participate in on-call support for production environments.

Design, develop, evaluate, and deploy AI-powered features for Zoom’s workforce engagement platform. Build LLM and prompt-based systems, integrate AI into production workflows, improve model accuracy through evaluation frameworks and feedback loops, and troubleshoot production issues. Collaborate with product managers, data scientists, and engineers while writing tested, documented, scalable software.

Build and maintain AI-driven workflows, agentic systems, automated operations tooling, cloud infrastructure, and CI/CD pipelines. Handle monitoring, incident triage, backend production issues, and field-operations support. Contribute across the full stack, communicate technical decisions to non-engineering stakeholders, and improve manual processes through automation. Required capabilities include cloud infrastructure, LLM and MCP development, Linux, CI/CD, full-stack engineering, and strong communication.

Develop and validate storage test software and tools based on industry specifications and firmware algorithms. Responsibilities include coding and debugging in C++, Python, JavaScript, and related technologies; designing storage test tools and APIs; automating builds, deployments, testing, and performance reporting; validating functionality and scalability; and creating technical documentation. The internship lasts six months, requires 40 hours per week, and is located in San Jose.

Develop and optimize GPU-accelerated deep learning inference software for LLMs, multimodal, and generative AI models. Contribute features to vLLM, SGLang, FlashInfer, and NVIDIA inference libraries; implement algorithms, tune performance across GPU architectures, and improve large-scale model serving. The role requires strong C/C++ software engineering, collaboration across framework teams, and expertise in profiling, debugging, performance modeling, GPU programming, or multi-GPU communication.

Develop, validate, and productize sensor calibration and egomotion algorithms for autonomous vehicles using classical and deep learning methods. Implement high-performance C++/CUDA code, build fleet monitoring tools, run in-vehicle tests, triage calibration issues, and collaborate with OEM partners to deploy calibration features across vehicle variants.

Design, develop, and maintain scalable distributed software systems using Java, microservices, big data technologies, and containerized infrastructure. Improve search performance through indexing, maintain batch and real-time pipelines, deliver tested and monitored production code, and participate in design and code reviews. Troubleshoot production issues, support continuous integration and delivery, implement automated testing, and mentor junior engineers.

Design, build, and manage large-scale software platforms for enterprise storage infrastructure. Develop REST APIs, provisioning tools, monitoring, telemetry, deployment automation, and self-service storage capabilities supporting NVIDIA engineering and AI/ML teams. Manage distributed backend systems with high availability, integrate cloud and container technologies, evaluate technologies, and coordinate infrastructure deployments. The role also involves operational support, documentation, and collaboration across a matrixed organization.

Define the technical vision and architecture for AI-native cybersecurity solutions using LLMs, agentic AI, retrieval systems, and modern machine learning. Lead scalable distributed-system design, establish engineering standards, partner with product and security stakeholders, and connect innovation to customer outcomes. Champion AI-native development practices, represent technical strategy to executives and customers, mentor senior engineers, and lead cross-functional initiatives without direct authority.

Build and operate ingestion, reconciliation, and reporting systems that reconcile card-network and partner settlement files against internal transactions. Deliver end-to-end features for traceability, accounting journals, tax and regulatory reporting, and compliance tooling while ensuring reliability, scalability, and data privacy.

Develop and maintain C++ and Python software models representing FPGA devices within Quartus Prime. Build high-performance APIs supporting synthesis, placement, routing, timing, and power analysis. Collaborate across hardware and software teams to integrate next-generation FPGA devices and create tools for FPGA device bring-up. The role requires strong software engineering, algorithms, data structures, communication, and experience with EDA or FPGA tools is preferred.

Designs and operates secure AI applications and backend microservices on AWS. Responsibilities include threat modeling, secure design reviews, vulnerability remediation, secure API development, AI guardrails, prompt testing, RAG pipelines, monitoring, audit logging, and incident readiness. The role collaborates with engineering, product, cloud infrastructure, and security teams to embed secure-by-design practices throughout the software development lifecycle.

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.

Design and develop high-performance dataplane software for the TeraWave satellite communications network. Responsibilities include C/C++ packet processing with DPDK and VPP, line-rate optimization at 100G/400G, routing and tunneling integration, hardware accelerator interfaces, distributed dataplane architecture, benchmarking, code reviews, technical leadership, and cross-functional collaboration.

Analyzes and resolves customer equipment software and systems issues, investigates software failures, provides ticket-based support, and communicates issue updates. Creates and tests software change notifications, feature procedures, and customer documentation. Participates in software specifications, design, testing, and reviews, while assisting with training junior engineers and product operations.