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About This Role
### Principal Machine Learning Engineer (MLE)
- JR\-161164
- Hybrid
- Toronto
- Dallas
- Technology
- Full time
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 Principal 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 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
- 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
The targeted pay range for this position in the following location is / locations are:
Canada \- Toronto Office TRO : 154,000 \- 232,000 CAD / Annual
United States \- Dallas Infomart Office DAI : 200,000 \- 300,000 USD / 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 benefits. Employees are eligible for bonus, and equity may be offered depending on the position.
Equinix Benefits
As an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.
Employee Assistance Program: An Employee Assistance program is available to all employees.
US Benefits: \- Insurance: You may enroll in health, life, disability and voluntary plans that are designed for you and your eligible family members. \- Retirement: You and Equinix may contribute to a retirement plan to help you plan for your financial future. \- Paid Time Off (PTO) and Paid Holidays: You will receive an accrued amount of PTO each pay period along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to change and may be subject to specific plan or program terms. Canada Core Benefits: \- Insurance: You may enroll in healthcare coverage that is designed to complement the provincial healthcare system, along with life, disability and optional benefit plans that are designed for you and your eligible family members. \- Retirement: You may also enroll in Equinix\-sponsored retirement or savings plans: Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax\-Free Savings Plan (TSFA). \- Vacation and Paid Holidays: Equinix offers both vacation and personal time, along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to specific plan or program terms, and to change at Equinix discretion.
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 form.
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
We use artificial intelligence in our hiring process. Learn more here.
This posting is a new position within our organization.
Salary Context
This $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Equinix, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($250K) sits 38% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Equinix AI Hiring
Equinix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $300K - $300K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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