Principal Product Manager, Model Customization, Open-Weight Models

Equinix

. Define Equinix’s model-selection framework and recommended-model catalogue .

Posted 11/4/2026full-timeUnited StatesLead💰 $158,000 - $286,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in model customization, including fine-tuning, retrieval-augmented generation, and evaluation methodologies. Proficient in managing AI/ML product lifecycles, ensuring compliance with security and regulatory standards while collaborating across engineering and business teams.

Highest-signal resume keywords

Model CustomizationFine-TuningPrompt EngineeringProduct Management in AI/MLGPU Capacity Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Model SelectionParameter-Efficient TuningSynthetic Data GenerationData CurationQuantizationReinforcement LearningEvaluation SkillsOpen-Weight ModelsContext EngineeringTask-Specific Evaluations

Soft Skills

Cross-Functional LeadershipCollaborationStrategic ThinkingCommunication

Tools & Technologies

PyTorchHugging FaceVLLMSGLangPython

Industry Keywords

AI/ML PlatformsData RightsModel GovernanceRegulatory ComplianceFinancial Services

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Define Equinix’s model-selection framework and recommended-model catalogue
Track model releases, benchmark models against Equinix workloads, and assess upgrade value
Set the model-customization playbook across prompting, retrieval, fine-tuning, preference optimization, reinforcement learning, distillation and quantization
Define tuning-data strategy, including curation, labeling, synthetic data, deduplication and leakage prevention
Own the roadmap for a shared model-customization platform covering data preparation, training, evaluation, registry, deployment and lifecycle management
Partner with infrastructure and engineering on GPU capacity, training and serving stacks, inference optimization and cost controls
Ensure tuned models are reproducible and supportable with versioned data, configurations, checkpoints, ownership and rollback
Work with vertical teams to scope use cases, select models, design tuning experiments and launch to production
Create reusable patterns, templates and services from successful implementations
Run model launch reviews covering quality, safety, cost, performance and operational readiness
Define pre-launch evaluations for quality, regression, safety, robustness, cost and latency
Measure tuned models in production and feed results into improved data and models
Report quality gains, cost savings, latency reductions and risk avoided
Establish standards for license review, model provenance, data rights, documentation, red-teaming and deployment approval
Ensure confidential and customer data meet Equinix security, privacy and regulatory requirements
Lead cross-functional strategy, specifications and executive briefs
In the first year, publish the model-selection framework, launch the shared customization platform, ship tuned open-weight models with at least two vertical teams, and establish governance processes

Requirements

What you’ll need

7+ years across product management, engineering or applied ML
3+ years as a product manager on AI/ML, data or infrastructure platforms
Hands-on experience customizing and shipping open-weight models in production
Experience with fine-tuning, parameter-efficient tuning, preference optimization, distillation or quantization
Deep practical knowledge of prompt and context engineering and retrieval-augmented generation
Experience selecting models for real applications, weighing quality, cost, latency, hardware and licensing
Strong evaluation skills, including task-specific evaluations and avoiding train/test leakage
Working knowledge of PyTorch, Hugging Face, vLLM or SGLang and GPU cost/capacity trade-offs
Comfort reading and writing Python, running experiments, and reviewing training and evaluation results
Ability to lead across engineering, science, infrastructure and business teams
A degree in computer science, engineering, machine learning or another quantitative field, or equivalent experience is nice to have
Experience with reinforcement learning from human or AI feedback, or reward modeling is nice to have
Experience with synthetic data generation and data curation at scale is nice to have
Experience with tool-use or agentic fine-tuning, or function-calling models is nice to have
Experience with open-weight model licensing, model governance or responsible AI programs is nice to have
Experience running models in private, hybrid or on-premises environments, or with data residency requirements is nice to have
Experience in regulated industries such as financial services is nice to have

Benefits

Comp & perks

Employee Assistance Program
US health, life, disability and voluntary insurance plans
US retirement plan contributions
US paid time off and paid holidays
Canada healthcare coverage
Canada life, disability and optional benefit plans
Canada Defined Contribution Pension Plan (DCPP)
Canada Group Retirement Savings Plan (RRSP)
Canada Tax-Free Savings Plan (TSFA)
Canada vacation, personal time and paid holidays
Bonus eligibility
Equity may be offered depending on the position
Disability accommodations in the employment process