LLM Researcher

Job Summary

Have you streamed a movie online, accessed an email, or utilized cloud services? If so, youve likely engaged with Equinix data centers in some capacity. Equinix operates International Business Exchange (Data Center) facilities where the information-driven world flourishes. Our Data Centers serve as a platform for many of the worlds leading brands to launch their next groundbreaking innovations.

As a Large Language Models (LLM) Researcher, you will be responsible for building and optimizing our Generative AI systems. You will evaluate and create state-of-the-art Generative AI models and develop agent workflows across multiple cloud platforms, including GCP, AWS, and Azure.

Responsibilities

• Research and implement advanced Large Language Models (LLMs)
• Collaborate with Generative AI Centre of Excellence leaders and Equinix business units to assist in deciding between purchasing off-the-shelf generative AI tools and building solutions from foundational models for various generative AI applications
• Utilize complex agents with platforms like Google Agentspace, Microsoft CoPilot, and Salesforce Agentforce
• Innovate and optimize the machine learning workflow, from data exploration and model experimentation to production deployments on cloud platforms such as GCP or Azure
• Architect LLM solutions that integrate agents built on different clouds or applications into a unified platform
• Proficiently use deep learning frameworks such as PyTorch and TensorFlow
• Develop model pipelines in a development environment, manage version control with Git, utilize GitHub Actions, containerize applications, and deploy them to virtual machines, App Engine, or Kubernetes clusters
• Possess in-depth knowledge of NLP fundamentals, including transformers, attention models, and text pre-processing
• Publish NLP or Machine Learning research papers in top AI journals or conferences
• Articulate research findings into patents
• Apply cutting-edge technologies and toolchains in big data and machine learning to build a robust machine learning platform on the cloud (MLOps)
• (Good to have) Envision, implement, and deliver production-level classical machine learning models (regression, classification, clustering), NLP models (sentiment analysis, summarization, chatbot/Q&A, information retrieval), and computer vision applications (image classification, object detection, semantic segmentation, and instance segmentation using YOLO V7, DDRNet, RFTM with pre-trained datasets like COCO and Cityscapes)
• Deploy machine learning models into production using cutting-edge deployment strategies and conduct A/B tests to objectively measure performance improvements
• Continuously innovate and optimize the machine learning workflow, from data exploration and model experimentation to production deployment
• Develop features, conduct tests, perform statistical analyses, and interpret results to drive insights

Qualifications

• PhD with 4+ years of experience, Masters with 3+ years, or Bachelors with 6+ years in Data Science, Computer Science, or Machine Learning
• Proficiency in Python programming is essential
• Strong understanding of software engineering principles and design patterns
• Experience with at least one major cloud platform
• Ability to effectively communicate analysis results and insights
• Excellent time management, communication, and organizational skills