Software Engineer Jobs in Sunnyvale, CA

Lead security engineering projects at Databricks, focusing on data protection strategies and systems management. Attract talent and represent security initiatives across the organization.

As a Senior Software Engineer with a backend focus, you'll design and build infrastructure for large-scale data and AI solutions at Databricks, addressing challenges across resource management and distributed systems.

Design and develop embedded software for networking platforms, participate in project phases, and collaborate with HW and SW teams to troubleshoot.

Develop high-performance GPS software for aviation, design and certify time-critical systems, and collaborate with hardware teams for advanced technology solutions.

Design, build, and operate scalable, production distributed systems for publisher onboarding, ad quality, and monetization. Gather requirements, write reusable code, break down work, act as on-call DRI, and improve availability, performance, and observability while leveraging cloud and AI/ML technologies.

Design, build, and operate high-scale experimentation platform components for AI systems. Lead architecture, produce reliable, secure, and performant services, mentor engineers, collaborate with PMs and SRE/privacy/security partners, maintain on-call operations, and drive automation, observability, and experimentation best practices.

Debugs complex software products through systematic testing, develops and maintains quality standards, writes test procedures, documents results, analyzes failures, and recommends corrective actions. Collaborates with development engineers to resolve issues and supports software integration across scientific computation, data acquisition, and processing systems. May lead small software test projects and serve as a resource for less-experienced colleagues.

Develops and maintains software quality standards through systematic testing of complex software products. Responsibilities include executing tests, writing test procedures, documenting and analyzing results, identifying corrective actions, collaborating with developers to resolve issues, and supporting software integration with scientific computation, data acquisition, and processing systems. May lead small software test projects and provide technical guidance to less experienced colleagues.

Design, build, and maintain highly scalable backend services and fleet management systems for autonomous vehicles. Partner with software, hardware, infrastructure, product, design, and operations teams to deliver reliable AWS-based microservices, vehicle communication systems, and asset management solutions. Establish high-availability infrastructure, support product lifecycles, mentor teammates, and promote engineering excellence for safety-critical commercial operations.

Design, implement, maintain, and test high-precision vehicle localization software for Aurora’s autonomous driving fleet. Develop pose estimation solutions using high-rate sensor data, verify system safety and efficiency, and collaborate with engineers and stakeholders across product teams. The role requires expertise in C++, Python, state estimation, sensor modeling, spatial transforms, linear algebra, and probabilistic estimation, with robotics, autonomous vehicle, machine learning, or GPU experience preferred.

Develop production-grade perception and machine learning systems for autonomous driving, including object tracking, sensor fusion, action and attribute recognition, and semantic understanding. Research deep learning models, analyze failures, optimize large-scale training pipelines, and deploy real-time solutions on Aurora’s Driver platform. The role also involves architectural strategy, software lifecycle ownership, and cross-functional collaboration across simulated and on-road testing.

Optimize deep learning networks for autonomous vehicles and large-scale data centers. Analyze and troubleshoot performance using profiling and roofline modeling, improve software architecture, latency, GPU memory usage, and throughput, and deploy scalable models with frameworks such as PyTorch or TensorFlow. Collaborate across engineering and research teams on large codebases, applying CUDA, C++, Python, and inference acceleration technologies in Linux/Unix environments.

Lead development of online mapping perception algorithms for Aurora’s autonomous driving system. Research and build deep learning models, develop verification and validation techniques, and collaborate with mapping, simulation, and safety teams. Build infrastructure and tests supporting deployment decisions, then integrate, test, and deploy production-ready solutions into the Aurora Driver codebase.

Define vehicle connectivity architecture and build reliable, low-latency vehicle-to-cloud communications for self-driving vehicles. Develop high-performance, thread-safe systems in C++ and Linux, address networking and observability, optimize data throughput, support field testing and fleet operations, mentor engineers, establish coding standards, and guide the connectivity roadmap.

Own Aurora’s internal SaaS and collaboration platforms architecturally, including roadmap, integrations, identity lifecycle, and platform governance. Build production software and automation in Python or TypeScript/JavaScript to eliminate recurring support work through APIs, workflows, self-service tools, and AI agents. Lead tool evaluations, rollouts, licensing strategy, and enterprise AI adoption while serving as an escalation point and partnering with IT, Security, Operations, and senior leadership.

Develop and optimize deep learning and machine learning perception models for autonomous vehicles. Address object detection, traffic-actor tracking, action recognition, and semantic scene understanding. Deploy production-ready software to real-time vehicle platforms, build scalable ML pipelines, analyze large datasets, and resolve failures through on-road and simulated testing. Collaborate with cross-functional engineering teams to deliver robust computer vision systems using 3D sensor data from LiDAR, Radar, and cameras.

Design, build, and scale cloud-native distributed systems and runtimes to deploy, run, fine-tune, and observe enterprise AI agents. Drive architecture, optimize model/infrastructure interactions, improve developer iteration tooling, enforce secure-by-design practices, and mentor engineering teams to deliver production-ready agent platforms.

Design and implement high-performance Vision AI pipelines and large-scale distributed services for processing video, image, and 3D data across edge and cloud. Build multi-modal perception, use simulation/synthetic data for testing, profile and optimize GPU-accelerated inference, collaborate with research and product teams, drive code quality and reviews, and mentor engineers to productionize vision models at scale.

Develop and extend functional and performance test strategies for NVIDIA DriveOS. Build software to automate verification across DriveOS components, collaborate with development and customer-facing teams, analyze defects to the code level, and lead effective fixes. The role requires software development experience in Python or C++, embedded systems and automotive sensor expertise, Linux proficiency, source control experience, and strong analytical and communication skills.

Design, develop, and verify chip-specific kernel drivers and embedded software for next-generation NVIDIA chips. Enable Linux and core operating systems across simulation, emulation, FPGA, and silicon platforms. Collaborate with hardware and software teams on platform bring-up, architecture, debugging, automation, and production deployment. Resolve complex hardware-software integration issues and independently drive projects from design through deployment.

Design and build a Kubernetes-native system that discovers, aggregates, and normalizes cluster topology data for provisioning systems and schedulers. Contribute to the open-source Topograph project, optimize GPU-to-GPU communication on NVIDIA hardware, and collaborate on interfaces between discovery logic, data models, and scheduler output.

Develop and execute platform test plans across servers, operating systems, firmware, and CUDA software. Build and debug automation frameworks and tests, perform reliability validation, troubleshoot Linux and server environments, conduct root-cause analysis, and manage defect lifecycles. Collaborate with partners and internal teams to resolve issues and improve production quality. The role also involves AI tools, NLP, LLM benchmarking, CI/CD, DevOps, virtualization, and enterprise server technologies.

Develop graphical user interfaces and supporting middleware for robotic surgical systems using Qt and embedded C/C++. Collaborate with designers and engineering teams on visual design, software architecture, prototyping, verification, debugging, and maintenance. Explore emerging user interface technologies and deliver robust, high-quality software throughout the product lifecycle.

Own and maintain HD maps infrastructure across products. Define map storage formats and distribute map data for UIs, data pipelines, storage, and SDKs. Collaborate with engineers and customers to design and implement mapping and localization solutions.

Own and develop a multi-environment compute platform for workload scheduling, resource allocation, and node lifecycle. Solve distributed systems challenges (GPU scheduling, autoscaling, resource efficiency), ensure production reliability and scalability for simulations, collaborate across teams, and drive architecture to expand orchestration company-wide.