Engineering Intern

January 9

Apply Now

Receive Emails with Similar Jobs

DDN

WebsiteLinkedInAll Job Openings

DDN is a global leader in AI data intelligence solutions, providing high-performance computing and sophisticated data management technologies. With a focus on accelerating AI deployments and advanced data analytics, DDN's products, including the Data Intelligence Platform and advanced storage systems, serve diverse sectors such as healthcare, financial services, and government. DDN is committed to transforming enterprise data infrastructure to leverage the full potential of AI and drive operational efficiency.

Artificial Intelligence • Data Storage • Machine Learning • Analytics • Enterprise AI

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

📋 Description

• DDN is currently seeking interns to join our Infinia Engineering Team, where you'll have the opportunity to work on cutting-edge data storage and management solutions designed for high-performance computing and artificial intelligence applications. • Responsibilities / Potential Projects: • Design and implement integration of data ingestion and streaming pipelines with open-source tools, like Ray Data, Mosaic Streaming, Tf.data, Torch Dataloader. • Design of optimization for training like asynchronous checkpointing, and inference, like K-V caching and LORAX. • Guide the integration of MLFlow with DDN’s Infinia product for comprehensive experiment tracking, model versioning, and deployment. • Drive the implementation and scaling of Retrieval-Augmented Generation (RAG) pipelines to enhance generative model performance. • Stay abreast of the latest developments in AIOps, AI frameworks, optimization, and accelerated execution. • Identify and implement solutions to optimize training and inference pipeline performance, runtime, and resource utilization on Infinia.

🎯 Requirements

• Working toward Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related fields. • Some experience in machine learning operations (MLOps) or related roles. • Some experience in building and scaling AI/ML pipelines. • Strong understanding of machine learning frameworks and libraries (TensorFlow, PyTorch, NVIDIA NeMo, vLLM, TensorRT-LLM). • Some understanding of cloud infrastructure (AWS, GCP, Azure) and distributed computing. • Some experience with containerization tools (Docker, Kubernetes) and infrastructure as code. • Excellent problem-solving and troubleshooting skills, with attention to detail and performance optimization. • Strong communication and collaboration skills.