Senior Data Scientist - Security and Networking Research

We're looking for a Senior Data Scientist to join a group that specializes in Security and Networking, in relation to ML/AI development. As a Senior Data Scientist you’ll have the opportunity to take an active part in the research and development of NVIDIA’s world-class networking and data center products. This role involves creative problem solving alongside engineering teams, and is key for the continued success of AI networking security.

Responsibilities

• Developing, implementing and improving models and algorithms across media types, whether time series, images, text, audio or video.
• Leveraging data pipelines to efficiently process and transform large volumes of data for training and inference purposes.
• Optimizing and fine-tuning models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.
• Creating efficient pipelines for model training, feedback and improvement.
• Applying alignment techniques and parameter efficient fine-tuning to improve model performance.
• Measuring and benchmarking model and application performance to drive improvements.
• Driving the gathering, building, and annotation of domain specific datasets for benchmarking and training.
• Collaborating closely with software and hardware engineers on new features and improvements. Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.

Requirements

• MS/PhD with expertise in Computer Science, Computer Engineering, Electrical Engineering or related field with a focus on Deep Learning or Machine Learning.
• 5+ years of experience in deep learning and machine learning in a production environment.
• Excellent programming skills in Python with software design fundamentals.
• Hands-on experience with deep learning development frameworks and libraries (e.g. TensorFlow, PyTorch).
• Experience with large scale production systems and pipelines, with a track record of developing production-grade models.
• Strong algorithm development experience, with knowledge of inference optimization techniques such as model distillation, quantization, pruning.
• Experience with algorithms including zero/few-shot learning, self-supervised and unsupervised learning and generative AI models for synthetic data creation.
• Experience with generative models, agents and RAG (including vector databases and reranking algorithms).
• Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT.
• Experience with tools for data processing and storage (e.g. Apache Spark, Hadoop, SQL databases, NoSQL databases).
• Security and networking background, with knowledge of security protocols, network architectures, firewalls, intrusion detection systems, and other relevant security and networking concepts.