Principal Product Manager, Model Customization & Open-Weight Models

Equinix•Redwood City, CA

About The Position

Equinix is hiring a Principal Product Manager to own the strategy, platform, and production launches for model selection and customization, with a focus on open-weight models. The role designs and manages the product lifecycle from concept through launch and ongoing operation, translating market opportunities and customer needs into differentiated products. It will establish how Equinix selects models for specific applications, maintain a recommended-model catalogue, and benchmark new releases against Equinix workloads. The role leads the approach to model customization, choosing among prompting, context engineering, retrieval, fine-tuning, preference optimization, reinforcement learning from feedback, distillation, and quantization according to the needs of each use case. It also sets the data strategy for customization and owns the roadmap for a shared platform supporting data preparation, training, evaluation, registration, deployment, and lifecycle management. The Product Manager partners with infrastructure, engineering, business, and domain teams to launch tuned models, improve evaluation and governance, and report business impact. In the first year, the role is expected to publish Equinix’s model-selection framework and benchmarked catalogue; launch a shared customization platform that takes teams from data through deployment; ship tuned open-weight models with at least two vertical teams and measure gains against baselines; and establish an efficient governance process for licensing, data rights, and deployment approval. The position is a product leadership role, not a research scientist or pure prompt-engineering role, and is accountable for measurable improvements in production model quality, cost, or latency. The targeted annual base pay ranges are USD 190,000–286,000 for Redwood City, USD 158,000–238,000 for Dallas, and CAD 158,000–236,000 for Toronto. Pay depends on job-related skills, experience, education, training, role, level, and location; the stated ranges exclude bonus, equity, and benefits. Employees are eligible for a bonus, and equity may be offered depending on the position. Benefits vary by country and include an Employee Assistance Program, insurance options, retirement or savings plans, and paid time off or vacation and paid holidays, subject to eligibility and plan terms.

Requirements

At least 7 years of experience across product management, engineering, or applied machine learning, including at least 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 through 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 these instead of training.
Experience selecting models for real applications and weighing quality, cost, latency, hardware, and licensing considerations.
Strong evaluation skills, including building task-specific evaluations, avoiding train/test leakage, and distinguishing genuine improvement from noise.
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.
Comfort reading and writing Python, running experiments, and reviewing training and evaluation results.
Ability to lead across engineering, science, infrastructure, and business teams without direct authority, and to explain trade-offs clearly to senior leaders.

Nice To Haves

Experience with reinforcement learning from human or AI feedback, or reward modeling.
Experience with synthetic data generation and large-scale data curation.
Experience with tool-use or agentic fine-tuning, or 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.

Responsibilities

Define model-selection criteria for each application, including task quality on Equinix evaluations, cost per task, latency, context length, tool-use ability, hardware footprint, license terms, and data-handling requirements.
Maintain a recommended-model catalogue spanning open-weight and proprietary models, with guidance on appropriate use cases.
Track model releases, benchmark models against Equinix workloads, and determine whether upgrades justify switching costs.
Set the playbook for model adaptation, including prompt and context engineering, retrieval, supervised fine-tuning, parameter-efficient methods such as LoRA, preference optimization, reinforcement learning from feedback, distillation, and quantization.
Guide teams toward the lightest customization technique that meets requirements, prioritizing prompting and retrieval before training where appropriate.
Define tuning-data strategy, including curation, labeling, synthetic data generation, deduplication, and prevention of leakage between training and evaluation data.
Own the roadmap for a shared customization platform covering data preparation, training, evaluation, model registry, deployment, and lifecycle management.
Partner with infrastructure and engineering teams on GPU capacity, training and serving stacks, inference optimization, and cost controls.
Ensure tuned models are reproducible and supportable through versioned data, configurations and checkpoints, clear ownership, and rollback capabilities.
Partner with business and domain teams to scope use cases, select models, design tuning experiments, and launch solutions into production.
Turn successful early projects into reusable patterns, templates, and services for other teams.
Run model launch reviews covering quality, safety, cost, performance, and operational readiness.
Define evaluations required before launch, including task-specific quality, regression against the base model, safety, robustness, and cost and latency targets.
Partner with the Agent Intelligence team to measure tuned models in production and use feedback to improve 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 for tuning complies with Equinix security, privacy, and regulatory requirements.
Serve as Equinix’s product leader for model selection and customization, influencing teams beyond direct reporting lines.
Write strategy documents, product specifications, and executive briefs to align engineering, data science, infrastructure, security, and business leaders.
In the first year, publish a benchmark-backed model-selection framework and catalogue, launch a shared customization platform, ship tuned open-weight models with at least two vertical teams with measured gains, and establish an efficient governance process.

Benefits

Employee Assistance Program available to all employees.
Insurance options, including health or healthcare, life, disability, and voluntary or optional plans for eligible employees and family members; coverage varies by country.
Retirement or savings plans, including employer and employee contributions in the U.S. and Equinix-sponsored pension or savings plans in Canada.
Accrued paid time off and paid holidays in the U.S.; vacation, personal time, and paid holidays in Canada.

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