Data Engineer Jobs in Sunnyvale, CA

Design and operate distributed inference systems for LLMs, build large-scale data pipelines, and debug production issues while collaborating with researchers and ML engineers.

Lead development of data pipelines powering ML for motion planning and decision making. Prototype pipeline and model improvements, fine-tune and evaluate models, deploy and validate models on fleet with monitoring, collaborate with perception/research/simulation teams, and mentor engineers in data-driven reasoning approaches.

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).

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

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

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

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, build, and scale open-source data infrastructure across collection, ingestion, storage, querying, and retrieval. Improve ingestion pipelines, expose performant query interfaces and integration hooks, and deploy reliable data platform features across cloud, hybrid, and on‑prem environments.

Design and support robotic and data-collection hardware, build large-scale robotic simulation environments for RL training, process human motion data, collaborate on research publications, and integrate algorithms with ADAS, data, and simulation engineering teams.

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.

Design, build, and modernize full‑stack web applications and microservices for a real‑time event-driven data and visualization platform. Develop and optimize streaming data pipelines (Kafka, Flink, Spark), integrate observability, caching (Redis), and AI agents, and collaborate with hardware verification teams for scalable, low-latency solutions.

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

The role involves leading developer relations for AI platforms, guiding partners on product integration, and fostering collaboration for ecosystem growth.

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.

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.

Design, deploy, and maintain a hybrid data infrastructure including Vertica, SQL Server, and PostgreSQL clusters; build and optimize ETL pipelines; automate Windows/Linux operations with PowerShell and Python; manage VMware hypervisor platform and storage; develop IaC, runbooks, and monitoring; lead incident response and capacity planning.

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.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

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.

As a Research Engineer, you conduct pioneering research in autonomous systems and develop models using large-scale data and advanced simulators, collaborating with scientists and contributing to high-quality publications.

Develop and optimize end-to-end data pipelines for autonomous driving, ensure data quality, and support model training through efficient data management processes.

Develop scalable data pipelines for autonomous driving, enhancing data extraction and labeling processes, and collaborating with cross-functional teams on ML models.

As an MLOps engineer, you will develop optimized data pipelines, enhance data engine architecture, and integrate foundation models while working closely with DoD customers.