Principal Product Manager, Model Customization & Open-Weight Models

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 - To