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Equinix

Global data center, colocation, interconnection provider

Principal Product Manager - Model Customization & Open-Weight Models

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • The candidate must have 7 or more years of experience across product management, engineering, or applied machine learning, including at least 3 years as a product manager on artificial intelligence/machine learning, data, or infrastructure platforms.
  • The candidate must have hands-on experience customizing and shipping open-weight models in production, such as through fine-tuning, parameter-efficient tuning, preference optimization, distillation, or quantization.
  • The candidate must have deep practical knowledge of prompt and context engineering and retrieval-augmented generation, and sound judgment about when to use these methods instead of training.
  • The candidate must have experience selecting models for real applications and weighing quality, cost, latency, hardware, and licensing.
  • The candidate must have strong evaluation skills, including building task-specific evaluations, avoiding train/test leakage, and distinguishing real improvements from noise.
  • The candidate must have working knowledge of model training and serving stacks, such as PyTorch, Hugging Face, and inference servers such as vLLM or SGLang, as well as GPU cost and capacity trade-offs.
  • The candidate must be comfortable reading and writing Python, running experiments, and reviewing training and evaluation results personally.
  • The candidate must be able to lead without authority across engineering, science, infrastructure, and business teams, and explain trade-offs clearly to senior leaders.

Responsibilities

  • Define criteria and processes for selecting models for each application, including task quality on internal evaluations, cost per task, latency, context length, tool-use ability, hardware footprint, license terms, and data-handling requirements.
  • Maintain a recommended-model catalogue covering open-weight and proprietary models, with guidance on when each model is appropriate.
  • Track new model releases, benchmark them against Equinix workloads, and determine whether an upgrade justifies its switching cost.
  • Set the model-adaptation playbook, covering prompt and context engineering, retrieval, supervised fine-tuning, parameter-efficient methods such as LoRA, preference optimization, reinforcement learning from feedback, distillation, and quantization.
  • Help teams select the lightest technique that meets requirements, prioritizing prompting and retrieval and using training when it provides clear value.
  • Define tuning-data strategy, including curation, labeling, synthetic data generation, deduplication, and preventing leakage between training and evaluation data.
  • 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 through versioned data, configurations and checkpoints, clear ownership, and rollback processes.
  • Work with business and domain teams to scope use cases, select models, design tuning experiments, and launch solutions to production.
  • Turn early successes into reusable patterns, templates, and services for other teams.
  • Run model launch reviews covering quality, safety, cost, performance, and operational readiness.
  • Define evaluations every model must pass before launch, covering task-specific quality, regression against the base model, safety, robustness, cost, and latency targets.
  • Partner with the Agent Intelligence team to measure tuned models in production and feed results back into improved data and models.
  • Report customization impact in terms such as quality gained, cost saved, latency reduced, and risk avoided.
  • Set standards for license review, model provenance, data rights, model documentation, red-teaming, and deployment approval.
  • Ensure confidential and customer data used in tuning is handled in accordance with Equinix security, privacy, and regulatory requirements.
  • Serve as Equinix’s product leader for model selection and customization, influencing teams outside the role’s direct management.
  • Write strategy, specifications, and executive briefs to align engineering, data science, infrastructure, security, and business leaders.
  • Publish Equinix’s model-selection framework and recommended-model catalogue, supported by benchmarks on internal workloads.
  • Launch a shared customization platform that takes teams from data preparation through deployment of an evaluated, tuned model.
  • Ship tuned open-weight models to production with at least two vertical teams and measure gains over the baseline.
  • Establish a fast governance process for licensing, data rights, and deployment approval.

Desired Qualifications

  • 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 working 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.

About the Company

Equinix

Global data center, colocation, interconnection provider

Equinix provides data center space, colocation, and interconnection services to businesses worldwide. Customers rent space to house servers and use interconnected networks inside Equinix data centers, enabling low-latency data transfer between networks and cloud providers. Unlike competitors that focus only on space, Equinix combines a global footprint with a large ecosystem of over 10,000 customers for direct, high-speed connections. Its goal is to enable fast, reliable digital operations and support customers’ digital transformation and multi-cloud strategies.

Company size
10,001+
Company stage
IPO
Headquarters
Redwood City, CA
Founded
1998

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