Principal Machine Learning Engineer (MLE)

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 tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective.

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
• 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 Tensor Flow
• 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
• 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

This posting is for a backfill position, meaning it is to fill an existing vacancy within our organization.

The targeted pay range for this position in the following location is / locations are: Canada - Toronto Office TRO : 112, CAD / Annual

Our pay ranges reflect the minimum and maximum target for new hire pay for the full-time position determined by role, level, and location. The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job-related skills, experience, and relevant education and/or training.

The targeted pay range listed reflects the base pay only and does not include bonus, equity, or…