Software Engineer Jobs in Sunnyvale, CA

Develop and ship behavior and trajectory planning software for L4 autonomous vehicles in dense urban environments. Build optimization, search, and sampling-based planning systems incorporating vehicle constraints, safety, comfort, and road rules. Integrate planning with perception, prediction, mapping, localization, and controls. Create fallback behaviors, simulation benchmarks, evaluation pipelines, debugging tools, and regression automation. Support vehicle integration, on-road issue resolution, safety validation, architecture, and cross-functional collaboration.

Build and ship scalable, performant AI-powered Copilot web experiences for millions of users. Design reusable frontend systems, deploy frontend infrastructure, and collaborate with AI researchers, product managers, and designers. The role emphasizes user-centric product development, safety, reliability, rapid iteration, and high-quality execution in a collaborative environment.

Designs and improves test automation frameworks, writes production-quality test code, and embeds quality engineering throughout the software development lifecycle. Responsibilities include unit, integration, end-to-end, performance, security, API, and database testing across distributed systems. The role partners with cross-functional teams, translates requirements into solutions, conducts code and design reviews, mentors junior engineers, and supports CI/CD automation in Agile/Scrum environments.

Design and develop sensor and compute systems for autonomous vehicles, build hardware-agnostic device drivers and runtime APIs, debug and optimize real-time systems, deploy reliable software with testing and monitoring, lead cross-functional sensor and compute projects, and mentor junior engineers.

Design, develop, and maintain sensor and compute systems and device drivers for autonomous vehicles. Architect reliable, high-performance onboard software, debug complex real-time systems, lead cross-functional sensor and compute projects, implement automated testing and metrics monitoring, collaborate with vendors, and mentor engineers.

The Software Engineer will design and implement video streaming solutions, build simulation frameworks, and collaborate on network metrics collection to improve teleoperations.

Build and ship AI-first growth experiments and scalable systems to drive acquisition, activation, engagement, and retention. Design and evolve A/B testing frameworks, instrumentation, metrics, and tooling; partner with Product, Data Science, Design, and Research to iterate quickly and scale successful patterns across global, high-traffic AI products.

Design, develop, and integrate real-time AI/ML sensor algorithms and sensor-control software for heterogeneous autonomous vehicles. Deploy containerized solutions to embedded Linux, perform computer-in-the-loop testing and profiling, support DoD field demonstrations, collaborate across sensor/tracking/autonomy teams, and drive simulation and post-test analysis to improve integrations.

Design and implement CUDA/C++ libraries to accelerate Apache Spark DataFrame and I/O operations, collaborate on distributed multi-node GPU solutions, contribute to open-source projects (RAPIDS, CCCL, UCX), and drive testing, CI, and cross-platform optimization for enterprise deployments.

Develop and maintain scalable 3D simulation software for Digital Twin and Synthetic Data Generation, collaborating with teams to optimize solutions.

Lead the architecture and evolution of Java-based SaaS platforms, distributed systems, APIs, and shared backend services. Drive complex initiatives across engineering teams, remain hands-on with implementation and troubleshooting, improve scalability, reliability, observability, security, and performance, establish engineering standards, and mentor senior and staff engineers.

Design, develop, optimize, and debug ultra low-latency C++ streaming systems and media pipelines (WebRTC, GStreamer, RTP). Analyze performance across CPU, memory, networking, and scheduling. Integrate streaming software with platform services across Windows, Linux, QNX, and embedded platforms. Define KPIs for networking and streaming quality and troubleshoot across software, OS, platform, and hardware boundaries.

Design and develop Java, Python, and SQL modules, REST APIs, dashboards, and services that improve engineering quality and operational efficiency. Build automated tests, collaborate with engineering teams, and deploy workloads to public cloud environments using CI/CD automation. Work with Spring Boot, cloud platforms, NoSQL and relational databases, Kafka data pipelines, logging, observability, and monitoring tools.

Designs, builds, and improves automated testing frameworks and production-quality test code across unit, integration, end-to-end, performance, and security testing. Partners with engineering and cross-functional teams throughout the SDLC, validates APIs and databases, supports CI/CD test pipelines, translates requirements into solutions, and contributes through code reviews, design reviews, debugging, documentation, and mentorship.

Lead architecture and technical vision for an AI-first, agentic evaluation platform that measures Copilot quality at scale. Design services, pipelines, tooling, and reliability measures to produce reproducible scorecards, support multi-turn/agentic evaluation, and ensure dependable outcomes across deep service dependencies. Mentor engineers and drive platform modernization, observability, guardrails, and large-scale evaluation strategies.

Design and implement large-scale, AI-first evaluation platform components for Copilot. Build reliable, observable systems for agentic evaluations, scoring, and scorecards. Ensure graceful degradation, dependency-aware gating, mentor engineers, act as DRI and participate in on-call to keep evaluation results trustworthy and timely.

Lead the architecture and implementation of mixed-criticality embedded virtualization for surgical robotic systems. Define partitioning, hypervisor configuration, deterministic inter-VM communication, device virtualization, observability, and safety verification. Evaluate Xen and QNX, analyze freedom from interference, lead failure-mode assessments, implement mitigations, and influence technical direction. Mentor engineers and lead cross-functional initiatives from prototype through completion in a regulated environment.

The role involves integrating machine learning and classical trajectory planners within safety frameworks for autonomous vehicles, focusing on architecture, planning, and control algorithms.

Develop and integrate embedded chassis-control software for autonomous vehicles and ECU hardware. Responsibilities include functional-safety development, model-based toolchain design, release planning, testing, vehicle bring-up, calibration, data analysis, debugging, and hardware/software integration. The engineer will validate AV components through on-vehicle, simulation, SiL, and HiL testing while collaborating with requirements, software, hardware, and systems engineers to support safe, reliable Level 4 autonomous vehicle operation.

Build and operate Aurora’s cloud infrastructure and developer platform. Responsibilities include managing Kubernetes upgrades, developing developer-facing tooling, operating Kubernetes services, improving development velocity, and collaborating across teams to gather requirements and advance platform projects.

Lead the technical direction for production engineering in NVIDIA DGX Cloud, focusing on Kubernetes operations, automation, and reliability for GPU clusters.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead end-to-end performance optimization for LLM and VLM inference on NVIDIA GPU systems. Analyze workloads, build performance models, profile systems, optimize CUDA kernels and serving runtimes, and tune batching, caching, quantization, speculative decoding, and parallelism. Establish reproducible benchmarks and regression gates while collaborating across model, framework, networking, and GPU architecture teams. Contribute improvements to open-source inference projects including TensorRT-LLM, vLLM, and SGLang.

Lead the design and development of Microsoft’s global-scale, Kubernetes-based cloud service platform. Provide technical leadership across teams, define requirements and architecture, mentor engineers, oversee project and release plans, and serve as a responsible on-call owner. Improve platform availability, reliability, security, observability, efficiency, and performance while standardizing service delivery across Microsoft cloud workloads.

Lead architecture and technical direction for an AI-first, agentic offline evaluation platform for Copilot. Design scalable, reliable evaluation pipelines, scoring, and observability for multi-turn agent behavior; mentor engineers and define metrics, guardrails, and reliability strategies under high load.