Senior Data Backend Engineer

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

NVIDIA’s team develops data engineering solutions that help AI developers in autonomous vehicle domains innovate quickly and effectively at scale. The Senior Data Backend Engineer designs and optimizes microservices and data pipelines for processing large volumes of autonomous-vehicle data, enabling data mining and AI training. The role uses expertise in big data processing and distributed computing to build efficient solutions and overarching architectures for video data curation, behavioral search, and AI dataset management.

Requirements

Experience developing high-performance, scalable software systems.
A master’s degree with 6 or more years of relevant experience, or a bachelor’s degree (or equivalent experience) with 8 or more years of relevant experience, in Computer Science, Computer Engineering, or a related technical field.
Strong programming skills in Python or Golang.
Proficiency with technologies such as Kubernetes, Helm, Hive, Parquet, SQL, and vector databases such as Milvus.
Strong architectural skills and a proactive, problem-solving mentality.
Experience in data mining and AI development.
Experience building ETL pipelines and working with big data engines.
Exceptional collaboration skills for working with system software and AI expert teams.
Eagerness to learn and adopt new technologies such as NVIDIA RAPIDS.

Nice To Haves

Prior experience with large-scale real-time streaming, augmented reality, or data curation.
Prior background with Spark.
Exposure to recent AI advances, including Large Language Models, Vision-Language Models, and Retrieval-Augmented Generation (RAG).
Innovative results, including patents, publications, or open-source contributions.

Responsibilities

Scope and build tools, microservices, workflows, and distributed applications to accelerate data mining and AI training.
Design and implement solutions for streaming, resilience, logging, security, authentication, workflow orchestration, and data management.
Deploy AI models.
Design and develop Retrieval-Augmented Generation (RAG) workflows that enable hybrid and agentic patterns.
Analyze and operationalize complex distributed systems for high performance.