Principal Machine Learning Engineer

Who are we?

Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.

A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.

Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Job Summary

As a Machine Learning Engineer, you will design, build, deploy, and scale machine learning and generative AI systems that power real-world products. You will work closely in AI Sidekick team and business teams to translate advanced ML and LLM capabilities into reliable, production‑grade solutions across multi‑cloud environments including GCP, AWS, and Azure.

This role blends applied machine learning, software engineering, and MLOps, with a strong focus on building robust, scalable systems rather than purely academic research.

Responsibilities

Design, develop, and deploy machine learning and Large Language Model (LLM)–based solutions for production use cases

Collaborate with Generative AI Center of Excellence leaders and business stakeholders to evaluate buy vs. build decisions for generative AI applications

Build and integrate agent-based workflows using platforms such as Google Agentspace, Microsoft Copilot, and Salesforce Agentforce

Develop end-to-end ML pipelines, covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring

Architect and implement LLM-powered systems that integrate agents and services across multiple cloud platforms into a unified solution

Optimize ML workflows for performance, scalability, reliability, and cost efficiency in cloud environments (GCP, Azure, AWS)

Implement and maintain MLOps best practices, including CI/CD, model versioning, experiment tracking, and automated retraining

Work extensively with deep learning frameworks such as PyTorch and TensorFlow

Containerize ML services and deploy them using Docker, Kubernetes, App Engine, or virtual machines

Apply strong knowledge of NLP fundamentals, including transformers, attention mechanisms, embeddings, and text preprocessing

Deploy and manage models in production, conduct A/B testing, and measure performance improvements using statistical methods

Develop features, run experiments, analyze results, and translate insights into actionable improvements

(Good to have) Build and deploy classical ML models (regression, classification, clustering), NLP applications (sentiment analysis, summarization, Q&A, chatbots, information retrieval), and computer vision solutions (image classification, object detection, segmentation using models such as YOLOv7, DDRNet, RFTM with datasets like COCO and Cityscapes)

Qualifications

PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years of experience in Machine Learning, Computer Science, Data Science, or a related field

Strong proficiency in Python for machine learning and production systems

Solid understanding of software engineering fundamentals, system design, and design patterns

Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)

Experience building and deploying production-grade ML systems

Strong communication skills with the ability to explain technical concepts and results to both technical and non-technical stakeholders

Excellent time management, collaboration, and organizational skills

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

We use artificial intelligence in our hiring process. Learn more here.

This posting is a new position within our organization.