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Equinix

Principal Product Manager, Model Customization & Open-Weight Models

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Dec 4, 2026
Posting language: English
Posted: Nov 4, 2026

Original job description

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.

A career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You’ll work across teams, influence key decisions, and help shape the path forward. 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

Designs, develops and manages the lifecycle of a product or group of products from concept to launch to end of life. Translates market opportunities and customer demand into viable products and services that differentiate Equinix in the market. Sets the vision and strategy for their product ensuring it is competitively positioned and customer-centric. Manages the product roadmap including features, upgrades and maintenance of the product or product line. Works cross functionally with user experience, engineering, operations, solution architects, marketing and others to design, build and launch new products and/or product features.

Responsibilities

Own model selection

Define how Equinix chooses models for each application. Criteria include task quality on our own evals, cost per task, latency, context length, tool-use ability, hardware footprint, license terms and data-handling requirements
Maintain a recommended-model catalogue across open-weight and proprietary models, with clear guidance on when each fits
Track new model releases, benchmark them against Equinix workloads, and decide when an upgrade is worth the switching cost

Lead model customization

Set the playbook for adapting models, from prompt and context engineering and retrieval to supervised fine-tuning, parameter-efficient methods (such as LoRA), preference optimization, reinforcement learning from feedback, distillation and quantization
Help teams choose the lightest technique that meets the bar. Prompting and retrieval come first, and training is used where it clearly pays off
Define the data strategy behind tuning, including curation, labeling, synthetic data generation, deduplication, and protecting against leakage between training and evaluation data

Build the platform

Own the roadmap for a shared model customization platform covering data preparation, training, evaluation, registry, deployment and lifecycle management
Partner with infrastructure and engineering teams on GPU capacity, training and serving stacks, inference optimization, and cost controls
Make tuned models reproducible and supportable, with versioned data, configs and checkpoints, clear ownership, and rollback

Partner with vertical teams to launch

Work with business and domain teams to scope use cases, pick models, design tuning experiments and launch to production
Turn early wins into repeatable patterns, templates and services that the next team can reuse
Run model launch reviews covering quality, safety, cost, performance and operational readiness

Measure and improve

Define the evaluations every model must pass before launch: task-specific quality, regression against the base model, safety, robustness and cost and latency targets
Partner with the Agent Intelligence team so tuned models are measured in production and feedback flows back into better data and better models
Report the impact of customization in terms leaders care about, such as quality gained, cost saved, latency reduced and risk avoided

Govern models responsibly

Set standards for license review, model provenance, data rights, model documentation, red-teaming and approval to deploy
Make sure confidential and customer data used in tuning is handled within Equinix’s security, privacy and regulatory requirements

Lead across the organization

Be Equinix’s go-to product leader on model selection and customization, and influence teams you don’t manage
Write the strategy, specs and executive briefs that align engineering, data science, infrastructure, security and business leaders

In your first year, you will

Publish Equinix’s model selection framework and recommended-model catalogue, backed by benchmarks on our own workloads
Launch a shared customization platform that takes a team from data to a deployed, evaluated, tuned model
Ship tuned open-weight models into production with at least two vertical teams, with measured gains over the baseline
Establish a governance process for licensing, data rights and approval to deploy that teams can move through quickly

Qualifications

7+ years across product management, engineering or applied ML, including 3+ years as a product manager on AI/ML, data or infrastructure platforms
Hands-on experience customizing and shipping open-weight models in production, such as fine-tuning, parameter-efficient tuning, preference optimization, distillation or quantization
Deep practical knowledge of prompt and context engineering and retrieval-augmented generation, and sound judgment about when to use them instead of training
Experience selecting models for real applications, weighing quality, cost, latency, hardware and licensing
Strong evaluation skills, including building task-specific evals, avoiding train/test leakage, and telling a real improvement from noise
Working knowledge of the model training and serving stack (for example PyTorch, Hugging Face, and inference servers such as vLLM or SGLang) and of GPU cost and capacity trade-offs
Comfort reading and writing Python, running experiments, and reviewing training and evaluation results yourself
Ability to lead without authority across engineering, science, infrastructure and business teams, and to explain trade-offs clearly to senior leaders

Nice to have

Experience with reinforcement learning from human or AI feedback, or with reward modeling
Experience with synthetic data generation and data curation at scale
Experience with tool-use or agentic fine-tuning, or with function-calling models
Experience with open-weight model licensing, model governance or responsible AI programs
Experience running models in private, hybrid or on-premises environments, or with data residency requirements
Experience in regulated industries such as financial services
A degree in computer science, engineering, machine learning or another quantitative field, or equivalent experience. Publications or open-source contributions are a plus

What this role is not

This is not a research scientist role, and it’s not a pure prompt-engineering role. You will own the product: the framework, the platform and the launches. You will be accountable for tuned models that are measurably better, cheaper or faster in production.

What success looks like

Every team knows which model to start with and why, and model choices are backed by Equinix’s own benchmarks
Teams reach for fine-tuning only when it pays off, and when they do, the platform makes it fast and repeatable
Tuned models in production beat their baselines on quality, cost or latency, and the gains are measured and reported
Model launches pass a clear, quick governance process with no surprises on licensing, data rights or safety
Equinix builds lasting expertise in running and improving its own models

Why this role matters

Choosing and adapting models well is becoming a core capability for any company building with AI. Open-weight models let Equinix put the right model on each problem, control cost and data, and build behavior that general-purpose models can’t match. You will make that capability real, repeatable and trusted across the company.

The targeted pay range for this position in the following location is / locations are:

United States - Redwood City Office GHQ : 190,000 - 286,000 USD / Annual

United States - Dallas Infomart Office DAI : 158,000 - 238,000 USD / Annual

Canada - Toronto Office TRO : 158,000 - 236,000 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 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 this 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.

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More about this job

Responsibilities

Own Equinix’s model selection strategy, customization platform, and production launches, including model evaluation, data strategy, infrastructure partnerships, and responsible governance. Partner with vertical teams to deliver measurable improvements in model quality, cost, latency, and operational readiness.

Requirements

Requires 7+ years of experience across product management, engineering, or applied machine learning, including 3+ years as a product manager in AI/ML, data, or infrastructure platforms, plus hands-on experience customizing and deploying open-weight models. Candidates should have practical expertise in model evaluation, prompt engineering, retrieval-augmented generation, Python, and model training and serving stacks, along with the ability to lead cross-functional teams.

Skills

Product Management
Open-Weight Model Customization
Model Selection
Prompt Engineering
Retrieval-Augmented Generation
Fine-Tuning
Model Evaluation
Python
PyTorch
Hugging Face
GPU Capacity and Cost Management
Model Training and Serving
Data Curation
Responsible AI Governance
Cross-Functional Leadership
Product Roadmap Management

Benefits

Employee Assistance Program
Health Insurance
Life Insurance
Disability Insurance
Retirement Savings Plans
Paid Time Off
Paid Holidays
Vacation

Visa sponsorship

Not detected in the job text

Keywords

Open-Weight Models
Model Selection
Model Customization
Fine-Tuning
LoRA
Preference Optimization
Reinforcement Learning From Feedback
Distillation
Quantization
Prompt Engineering
Context Engineering
Retrieval-Augmented Generation
Synthetic Data
Model Evaluation
Python
PyTorch
Hugging Face
vLLM
SGLang
GPU Infrastructure
Inference Optimization
Model Registry
Data Curation
Model Governance
Responsible AI
Model Licensing
Data Rights
Red-Teaming
Agentic Fine-Tuning
Data Residency

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