Engineering Intern

February 28

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DDN

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DDN is a global leader in AI data intelligence platforms, providing high-performance data management solutions designed to optimize and accelerate AI and computing tasks. With over two decades of experience, DDN specializes in delivering cutting-edge technology for data-intensive applications across various industries, including healthcare, financial services, and telecommunications. Their comprehensive solutions include state-of-the-art hardware and software systems that empower enterprises to effectively manage complex datasets and drive innovation through artificial intelligence.

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

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💳 Fintech

💰 $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. • 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.