Software Engineer Jobs in Sunnyvale, CA

Build and maintain core ML infrastructure: design and implement ML training, optimization, validation, and deployment pipelines; operate an in-house ML inference and compiler platform; implement testing, monitoring, and observability for model lifecycles; collaborate with cross-functional teams to deploy optimized models to vehicle hardware.

The role involves owning and maintaining HD maps infrastructure, defining storage formats, and collaborating with engineers and customers to enhance map-related features.

Design and scale simulation infrastructure and AI agentic workflows to analyze large-scale simulation results, improve simulation-to-reality fidelity, and validate autonomous vehicle systems. Architect pipelines, metrics, and evaluation frameworks; collaborate with cross-disciplinary teams to reproduce and resolve real-world issues and ensure quality and regulatory compliance.

Develop and maintain Java-based machine control software for semiconductor and display equipment. Design and manage microservices, integrate solutions with systems and application engineering teams, analyze requirements, create functional specifications, and support testing and troubleshooting. Collaborate with internal, external, and global teams while communicating project status and resolving complex software problems using strong computer science fundamentals, algorithms, data structures, and design patterns.

Research, develop, and productionize motion-planning algorithms for autonomous trucks. The role covers decision-making, trajectory generation, optimal control, uncertainty-aware planning, and integration with perception, prediction, controls, and safety systems. Engineers use C++ optimization frameworks and robotics tooling to build safe, comfortable, reliable behavior for complex driving scenarios. Responsibilities also include system diagnostics, algorithm tuning, technical design reviews, and cross-functional collaboration in safety-critical environments.

Seeking a Staff Storage Engineer to design and build control plane for an AI storage platform, focusing on scalability and performance across diverse environments.

Lead design and implementation of GPU-accelerated libraries and integrations for Apache Spark. Develop Java/Scala and CUDA/C++ components for I/O and DataFrame operations, enable interoperability with table formats and metastores, contribute to open source projects, collaborate on distributed systems at scale, and optimize cross-platform CUDA/C++ libraries and CI/testing infrastructure.

Lead technical architect for OCI Networking platform services. Drive design and implementation of scalable distributed systems for routing, workload placement, and fleet lifecycle management. Partner across teams, mentor engineers, lead architecture and operational excellence, and own CI/CD and release automation for production-grade cloud infrastructure.

Build and ship production web experiences for databricks.com using an AI-native platform. Implement components and page systems, integrate with CMSs, participate in code review, and embed AI tools across the engineering lifecycle to improve discovery, rendering, and deployment.

Develop low-level embedded flight software and firmware for autonomous aviation systems. Responsibilities include C/C++ requirements, design, coding, unit testing, requirements-based testing under DO-178C, bare-metal and hardware initialization, networking, protocol drivers, memory interfaces, Python integration tests, and continuous integration infrastructure. The role supports aircraft demonstrations, higher-level capabilities, certification, and product delivery.

Develop and verify CPU SoC boot firmware for pre- and post-silicon platforms. Define and decompose functional and safety requirements, perform failure analysis (FMEA/DFA), implement secure boot features, collaborate across teams, and support silicon validation and productization.

Lead pre-silicon system software bring-up and validation on simulation platforms. Develop automation, tools, and workflows to improve simulation-based development, debug complex cross-stack issues, enable teams to use virtual platforms, and drive platform readiness for new chips in early-development environments.

Lead and execute perception R&D and engineering for factory robots: define multi-year roadmap, architect perception pipelines (detection, segmentation, 6-DoF pose, scene understanding), drive sensor fusion and calibration, establish data/model workflows and deployment practices, integrate with planning/control, hire and mentor engineers, and evaluate research for production adoption.

The role involves designing solutions for autonomy, developing products, and collaborating with experts in various fields while mentoring engineers.

Design, implement, and improve NVIDIA’s CUDA Driver and programming models, including CUDA Graphs and GPU kernel scheduling for AI/ML workloads. Lead cross-team development, define CUDA API improvements, develop well-tested code across operating systems, and address system-level challenges involving operating systems, memory, threads, and GPU architecture.

Develop and modernize a Windows GUI application: implement a resizable main dialog, responsive controls and fonts, persist settings on exit, ensure HPE-compliant UI elements, and perform additional GUI-related enhancements using WinAPI, MFC, C/C++ in Visual Studio.

Design and optimize production machine-learning runtime pipelines for autonomous vehicle motion planning. Build high-performance C++ inference, feature extraction, trajectory generation, and orchestration components for embedded platforms. Develop validation, safety guardrails, fallback mechanisms, testing, monitoring, debugging, and observability tools. Investigate complex runtime failures and optimize latency, throughput, memory, and multithreaded performance while collaborating with machine-learning and systems engineering teams.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Design, build, and optimize multi-hop data center fabrics for large-scale AI inference. Work across userspace, kernel, drivers, ASIC SDKs, firmware, and optics to tune latency, bandwidth, and reliability. Lead fabric bring-up, telemetry, automation, validation, and operational best practices while mentoring teammates and partnering with hardware, platform, and manufacturing teams.

As a Software Engineering Intern, you will build foundational code and data processing systems, tackling challenges at the intersection of AI, code understanding, and data analytics.

Design and develop complex software solutions for semiconductor equipment, lead projects, define specifications, troubleshoot software problems, and communicate with customers.

Build and lead the inference orchestration layer for a globally distributed ML inference platform. Architect and implement highly available, low-latency systems, own reliability and performance, write production code, drive incident response, observability, capacity planning, and collaborate with ML, infrastructure, and cloud teams on scalable designs.

As a Senior Full Stack Software Engineer at Doxel, you will design and develop applications for project management, focusing on complex data handling and intuitive UI. Your role involves collaborating across teams and providing technical leadership in software projects.

Lead architecture, development, and deployment of software for spectroscopy and analytical instruments. Build embedded Linux control systems, data acquisition software, APIs, drivers, services, automation tools, and cloud-connected applications. Collaborate with hardware, firmware, optical, systems, manufacturing, and product teams; resolve complex integration issues; improve software quality through testing and CI; support calibration, manufacturing, diagnostics, and product planning; and mentor junior engineers.

Design, integrate, and deploy autonomy and vehicle intelligence across hardware and software for UAVs. Integrate sensors, autopilots, and datalinks on embedded Linux, conduct SIL/HIL and live flight tests, troubleshoot system issues, collaborate with vendors and DoD stakeholders, and support on-site demonstrations and data collection.