Data Engineer Jobs in Sunnyvale, CA

Build and maintain large-scale ETL and ML infrastructure to automate data selection, labeling, training, and testing loops. Connect real-world and simulation data to enable continuous model improvement, collaborate with modeling teams, and mentor junior engineers to scale data-centric development.

Responsible for designing and developing scalable data pipelines, storage systems, and monitoring tools for ML data infrastructure, ensuring system integrity and performance evaluation.

Develop large-scale data processing systems for autonomous driving, including storage, dashboards, and data pipelines, while ensuring integrity and resilience.

Design and develop robust data processing systems and pipelines, implement data storage solutions, and ensure system integrity through testing and monitoring.

Design and develop data pipelines for autonomous driving systems, create storage for data metrics, build dashboards, and maintain data integrity, focusing on ML components.

Design and implement cloud-native data management services (catalog, metadata, dataset/checkpoint lifecycle) for exabyte-scale GPU workloads. Build backend systems on Kubernetes and major cloud providers, collaborate with research and cross-functional teams, document architecture, and drive integration with storage/compute innovations (GPU Direct Storage, DPU).

Design and optimize high-performance C++/CUDA libraries for GPU-based data processing. Improve multi-node GPU communication (UcxExchange), optimize Presto GPU query performance, and lead enhancements across Presto, Velox, and cuDF projects for efficient DataFrame and database acceleration.

Lead architecture and technical direction for data integrations moving clinical and financial data at scale. Design orchestration, pipelines, data stores, observability, security/compliance, and mentor engineers while owning end-to-end cross-system initiatives and reliability.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Design and evolve global compute and storage architecture for large-scale GPU training and inference across multi-cloud and data centers. Enable petabyte-scale data federation, cross-region GPU job execution, advise leadership on cloud strategy, and provide technical leadership and mentorship to advance AI model development and deployment.

Lead design, build, and maintain scalable data pipelines and infrastructure. Collaborate with analysts and data scientists, ensure data quality, optimize performance, troubleshoot issues, document architecture, and mentor junior engineers.

Design and build data infrastructure and data pipelines, focusing on big data processing, modeling, and automation to support engineering teams at NextSense.

Design, develop, and optimize GPU- and CPU-accelerated image and data-processing libraries. Perform performance tuning, benchmarking, and optimization across architectures; collaborate with product management and partners; provide technical leadership and guidance to library engineers; and improve library performance and maintainability.

Lead strategic ISV partnerships to integrate NVIDIA software and SDKs into partner data platforms. Drive technical and GTM alignment, influence product strategy, recruit and lead cross-functional teams, promote NVIDIA solutions to architect and developer communities, and surface partner needs to internal product and engineering teams.

Design and implement real-time MAC data-plane software for high-throughput satellite links. Build packet-processing pipelines, queue and buffer management, retransmission and loss recovery, and integrate with PHY, modem, FPGA, DMA, and SoC hardware. Optimize for low latency and high throughput using zero-copy, scatter-gather, lock-free structures, and hardware offload. Lead architecture through prototyping, hardware integration, performance qualification, and operational deployment while diagnosing timing and synchronization issues.

Design, build, and maintain a petabyte-scale open-source data lakehouse and end-to-end data pipelines (ingest → transform → consume) ensuring performance, reliability, and data quality for analytics workloads.

Design, build, and operate large-scale offline data infrastructure and pipelines for ML training and experimentation. Improve reproducibility, observability, performance, and cost-efficiency across batch and streaming workloads while integrating orchestration systems and partnering with ML engineers.

Design and implement scalable data infrastructure and production data transformations. Lead and mentor a small Data Engineering team, ensure quality delivery, collaborate with Product, Data Analysts, and Data Scientists to operationalize algorithms and build robust data repositories.

As a Data Engineer at MAI, you will develop and maintain data platforms, design ETL/ELT processes, ensure data quality, and collaborate with teams to optimize data utilization for analytics and ML applications.

The Sr. Software Engineer will own the perception data infrastructure for autonomous vehicles, dealing with large 3D datasets and ensuring high data quality for ML models. Responsibilities include architecting core data platforms, optimizing real-time data rendering, and collaborating across functions to enhance engineering efficiency.

The Data Infrastructure Engineer will design, build, and operate scalable data infrastructure systems, ensuring reliability and performance while collaborating with various teams at OpenAI.

Design, build, and maintain scalable data processing pipelines and infrastructure (ETL/ELT). Implement large-scale data processing (Spark, MapReduce, Kafka), support stakeholders with analysis and reporting, ensure secure multi-region data handling, and optimize data delivery for analytics and business insights.

Own and improve analytics datasets and ETL pipelines powering an internal product analytics AI agent. Build retrieval, knowledge, evaluation, and backend agent workflows. Improve retrieval and answer quality, develop evaluation systems, and create user-facing features. Partner with data scientists to turn analytics needs into scalable capabilities and help set roadmap and technical direction for the data and quality side of the platform.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.