Data Engineer Jobs in Santa Clara, CA

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 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.

Design and implement test automation for instrument software, data pipelines, cloud analytics, and AI/ML outputs. Build data validation frameworks, schema and pipeline checks, integrate tests into CI/CD, validate ELN/LIMS interoperability, ensure traceability and regulatory Quality System compliance, and contribute to test solution architecture and orchestration.

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 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.

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.

Early-career role to build, operate, and improve cloud data/analytics platforms (Fabric, Snowflake, Databricks). Support platform operations, CI/CD for analytics assets, onboarding for BI tools (Power BI/Tableau), runbooks, monitoring, incident response, and basic security/governance. Work under senior mentorship and contribute to automation, deployment templates, and performance triage.

Lead architecture, operation, and improvement of cloud data and analytics platforms (Fabric, Snowflake, Databricks). Own CI/CD for data assets, BI delivery (Power BI/Tableau), reliability, monitoring, incident response, security, and governance. Enable teams through standards, runbooks, and onboarding; troubleshoot performance and ensure production stability.

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.

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

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.

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

Develop and maintain big data solutions using Hadoop, HBase, MongoDB and Core Java on Linux. Implement and support scalable data processing pipelines; contribute to big data architecture and platform tasks.

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

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

Own and improve annotation systems, workflows, and tooling to produce high-quality training data. Build Python and web tools, integrate model-assisted labeling and active learning, implement data quality checks, support dataset versioning and evaluation, and coordinate cross-team efforts to measure and improve annotation throughput and model impact.

Build and operate production pipelines that convert raw infrared satellite frames into calibrated, orthorectified, analysis-ready products. Optimize pipeline latency and scale, implement workflow orchestration, build monitoring and operational tooling, collaborate with remote sensing scientists to productionize algorithms, participate in on-call rotations, and diagnose and fix data quality and processing failures.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Lead the design and optimization of enterprise-scale data warehousing and analytics solutions, utilizing Snowflake and advanced SQL for global business operations and decision-making.

The role involves developing large-scale data processing pipelines, automating data selection, and collaborating on ML model improvements to enhance vehicle performance and safety.