Data Engineer Jobs in Sunnyvale, CA

Design and operate petabyte-scale end-to-end data pipelines for autonomous driving: onboard upload, cloud preprocessing, dataset production, and model training input. Build data cleaning, annotation QA, lineage, versioning, metadata management, and high-throughput distributed processing. Optimize I/O, memory, transmission, and containerized deployments; support cross-team collaboration and algorithm iteration.

As a Software Engineer focused on Data at Speechify, you'll manage data collection and ingestion for model training, enhance cloud infrastructure, and collaborate closely with AI scientists to optimize data quality and cost-efficiency for next-gen products.

The Software Engineer will enhance data collection and ingestion pipelines, operate cloud infrastructure, and collaborate on AI datasets for model training.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

The Principal Software Engineer will oversee technical readiness for NVIDIA's GT data generation, resolving upstream blockers and ensuring quality across sensors and data platforms while collaborating with teams in the US and China.

Lead the architecture, design, and implementation of a high-performance data processing engine focused on large-scale data processing.

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.

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, 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 optimize edge and cloud data pipelines to run perception models, integrate foundation models for labeling and QA, scale data engine architecture, and develop MLOps tooling. Collaborate with DoD customers and use software-in-the-loop and hardware-in-the-loop testing to accelerate model iteration and deployment to edge systems.

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.