Data Engineer Jobs in San Jose, CA

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Design, build, and maintain large-scale data pipelines and APIs for Ventura OS. Optimize data processing workflows, integrate ML models into production, ensure data quality and observability, and collaborate across teams to drive architecture and data infrastructure decisions for streaming and content recommendation use cases.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Design and build scalable, fault-tolerant batch and streaming data pipelines and data models for Roku devices and clients. Maintain petabyte-scale data warehouse, ensure data quality, optimize performance, debug large clusters, and collaborate across teams to deliver trusted analytics products and influence roadmap.

The Senior Data Analysis Engineer will analyze autonomous driving product performance, improve testing efficiency, automate metric generation, and collaborate with engineering teams to enhance product quality and insights.

The Principal Software Engineer will develop backend systems using Rust or Go, debug integrations, manage middleware components, and optimize data flows while following SDLC practices and collaborating with teams.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

The Sr Staff Engineer will architect and manage the data infrastructure, ensuring reliability and performance for data engineering and machine learning teams, while automating processes and monitoring systems.

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.

Collect, organize, and analyze large datasets to identify trends for IC power management systems. Develop and maintain data models, dashboards, and reports using machine learning and RFM analysis. Collaborate with cross-functional teams to deliver data-driven insights. Perform thorough data cleansing, validation, and transformation to ensure data accuracy and integrity.

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.

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

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).

Lead design, build, and maintenance of scalable data pipelines, sandbox databases, and analytics dashboards. Investigate and resolve complex data discrepancies, perform ETL/ELT, data modeling, and performance tuning using Databricks, Starburst, Kafka, SQL, and Python. Partner cross-functionally, enforce data governance, document processes, and mentor team members to support research prototyping and business insights.

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.