Principal Product Manager, Model Customization Weight Models

Position: Principal Product Manager, Model Customization & Open-Weight Models
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…