Software Engineer Jobs in Sunnyvale, CA

Develop statistical and machine learning techniques, quantitative models, and scalable automation for evaluation pipelines. Build data-processing infrastructure, improve evaluation efficiency, and solve ambiguous problems across statistics, ML, and large-scale systems. Collaborate with engineers, data scientists, product managers, and leadership while maintaining code quality in a complex codebase.

Set the technical vision and multi-year architecture for hyperscale build infrastructure. Lead build-system migrations, improve C/C++ build speed and reliability, establish reproducible containerized environments, operate CI and build tooling, define engineering metrics, influence organization-wide standards, and mentor engineers.

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.

Design and lead cloud and in-car data mining strategies for autonomous vehicle perception and end-to-end models. Build AI-driven software to accelerate data mining, collection, delivery, and corner-case resolution. Develop operational processes using big data, statistics, and machine learning to ensure high-quality training data from production and collection vehicles. Collaborate with platform teams and balance cost, engineering quality, and delivery timelines.

Develop and scale production AI infrastructure supporting large GPU clusters and workloads. Responsibilities include Kubernetes operator and API development, GPU resource scheduling, cluster operations, node health monitoring, telemetry-based reliability improvements, incident analysis, and distributed systems engineering. The role collaborates across teams to improve the performance, availability, and scalability of NVIDIA’s DGX Cloud platform.

Design, build, and maintain high-performance, scalable Java backend services and microservices for Walmart's catalog platform. Troubleshoot production issues, perform root-cause analysis, mentor junior engineers, work in agile teams, and contribute to cross-functional projects while leveraging cloud, CI/CD, NoSQL, and generative AI/ML technologies.

Develop core libraries and tools for scalable agentic applications. Responsibilities include optimizing agent frameworks and harnesses, benchmarking performance, improving latency and cost, integrating RAG systems and vector databases, supporting GPU workflows, designing cross-language APIs, debugging runtime internals, and collaborating with open-source communities. The role also involves building evaluations, observability, middleware, guardrails, and other systems that improve agent quality, developer productivity, and deployment reliability.

Sets the technical direction for autonomous vehicle behavior validation, connecting requirements, safety claims, simulation, metrics, coverage, acceptance criteria, and release evidence. Leads scalable evaluation across synthetic tests, road data, and counterfactual references; guides validation tools, pipelines, workflows, and analysis products. Coordinates cross-functional technical reviews and translates complex results into launch and monitoring recommendations.

Build and ship driver-facing AI experiences for GM vehicles using Kotlin and Android Automotive. Own features end to end across conversational behavior, on-device fulfillment, vehicle integration, UI, connected services, validation, and iteration. Prototype and productionize intelligent capabilities, diagnose issues across application and cloud boundaries, establish engineering patterns, collaborate cross-functionally, mentor engineers, and influence the roadmap for in-vehicle AI products.

Sets technical direction for counterfactual autonomy behavior validation, including scene reconstruction, human and road-user response modeling, controlled scenario variation, simulation pipelines, outcome estimation, and validation. Converts real incidents into reproducible, auditable scenarios and establishes confidence, traceability, and appropriate use of simulation results. Leads cross-functional work across safety, simulation, human factors, legal, product, and operations to produce engineering actions, safety-case evidence, and release recommendations.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build and own distributed runtime infrastructure for Moveworks’ AI agents, including agent orchestration, long-running session management, event-driven messaging, structured concurrency, observability, caching, and state layers. Design reliable, scalable backend systems supporting multi-step workflows, tool calls, human interactions, crash recovery, real-time streaming, and low-latency responses for enterprise users.

Build and own distributed runtime infrastructure for Moveworks AI agents, including orchestration state machines, session leasing and recovery, event-driven messaging, asynchronous concurrency, observability, caching, and state layers. Design production backend systems that handle high-scale real-time traffic while ensuring correctness, low latency, fault tolerance, traceability, and graceful recovery across long-running agent workflows.

Build and own distributed runtime infrastructure for AI agents, including orchestration state machines, session management, event-driven pipelines, structured concurrency, observability, caching, and state layers. Design highly reliable, low-latency backend systems using Python or Go, DynamoDB, SQS, Kafka, Redis, gRPC, and OpenTelemetry. Handle long-running workflows, leases, checkpointing, concurrency, streaming, tracing, and production-scale traffic.

Build and own distributed runtime infrastructure for AI agents, including orchestration engines, long-running session management, event-driven pipelines, concurrency systems, observability, caching, and state layers. Design highly reliable, low-latency backend systems using Python or Go, DynamoDB, SQS, Kafka, Redis, gRPC, and OpenTelemetry. Handle distributed coordination, crash recovery, streaming, tracing, and production traffic at scale.

Build and operate reliable, high-performance distributed backend systems for Nuro’s autonomous ride-hailing product. Responsibilities include ride planning, vehicle matching, real-time trip execution, partner API integrations, and telemetry pipelines displaying live vehicle data. The role requires end-to-end product ownership, strong GCP experience, system design expertise, and technical leadership through mentoring and architectural decision-making.

Troubleshoot complex software issues on customer sites, manage expectations and prioritize requests, validate customer scenarios, engage with product teams for readiness, develop and deliver software training, and mentor junior engineers.

Build production-grade automation for diagnosing and resolving CI/CD failures, identifying regressions and problematic merges, and improving developer time-to-signal. Design scalable compute and storage across cloud and on-premises environments, lead release engineering practices, integrate AI with deterministic automation, and promote tooling and engineering best practices across development and quality teams.

Design and build scalable backend and data systems supporting Uber’s global marketplace. Lead high-impact engineering projects, establish frameworks and best practices, resolve architectural challenges, oversee incident management, and collaborate across Product, Science, and Operations. Mentor engineers, drive technical roadmaps, and deliver reliable systems optimized for performance, correctness, and large-scale distributed workloads.

Develop platform and system software for NVIDIA GPU and SOC products, including kernel device drivers, firmware interactions, health monitoring, observability platforms, backend data pipelines, and production applications. Build scalable metrics, logging, and tracing systems while collaborating across software, cloud, data center, and product teams. Apply expertise in Linux internals, operating systems, computer architecture, concurrency, performance profiling, CPU-GPU interactions, PCIe, memory hierarchy, and AI workloads.

Build and operate production-grade AI, data, and distributed systems for fraud intelligence. Responsibilities include real-time data pipelines, high-throughput architecture, LLM-powered agentic workflows, AI tooling, rule and label automation, ML pipeline productionization, privacy safeguards, and cross-functional collaboration. The role requires strong Python engineering, distributed systems, cloud infrastructure, big data technologies, and machine learning fundamentals.

Lead Windows low-level software development for silicon bring-up, platform validation, and product enablement. Prototype and evaluate hardware/software designs, debug system-level issues across silicon/firmware/drivers, analyze performance, and build scalable automation, diagnostic, and regression test frameworks in collaboration with cross-functional and external partners.

Develop platform software for SOCs, including drivers, BSP, CI/CD automation, OS and multimedia driver bring-up, debugging, and system-level triage. Collaborate with OEMs and global engineering teams across the full product lifecycle from pre-silicon modeling through production. Contribute to architecture definition, boot, security, reliability, resiliency, and embedded-system software for Windows and Linux platforms.

Design and implement control plane software spanning SDN, ground systems, satellites, and terminals. Build scalable services and APIs for provisioning, configuration, automation, telemetry, and network operations. Address dynamic network conditions and integrate with cloud and on-premises platforms. Collaborate across data plane, platform, embedded systems, product, and quality teams while contributing to architecture, implementation, testing, debugging, and system integration.

Lead the technical vision, architecture, and implementation of the Data Ocean platform, building high-performance distributed storage capabilities across object, file, and block storage. Develop production C/C++ systems for caching, namespaces, tiering, replication, metadata, and data mobility. Drive architecture reviews, performance optimization, testing, and operational excellence while mentoring engineers and coordinating technical execution across global Engineering, Product, Architecture, cloud, and DevOps teams.