{"id":3770067,"url":"https://alion.io/job/equinix-principal-product-manager-model-customization-open-weight-models","title":"Principal Product Manager, Model Customization & Open-Weight Models","company":{"id":7655,"name":"Equinix","domain":"equinix.com","url":"https://alion.io/company/equinix","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":79,"open_postings":28,"ghost_share":0,"stale_share":0.821,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Product","role_family":"Product","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redwood City, United States","Toronto, Canada","Dallas, United States"],"countries":["US","CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":190000,"max":286000,"currency":"USD","period":"year","gross":null,"usd_annual":286000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Context Engineering","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LoRA","optional":false},{"name":"Model Distillation","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"SGLang","optional":false},{"name":"Synthetic Data","optional":false},{"name":"vLLM","optional":false},{"name":"Machine Learning","optional":true},{"name":"PEFT","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Reward Modeling","optional":true}],"status":"live","first_seen_at":"2026-11-04T21:20:16Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-06T09:53:15Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Who are we?\nEquinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, lifeand planet.\nA 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\nDesigns, 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.\nResponsibilities\nOwn model selection\nDefine 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\nMaintain a recommended-model catalogue across open-weight and proprietary models, with clear guidance on when each fits\nTrack new model releases, benchmark them against Equinix workloads, and decide when an upgrade is worth the switching cost\nLead model customization\nSet 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\nHelp teams choose the lightest technique that meets the bar. Prompting and retrieval come first, and training is used where it clearly pays off\nDefine the data strategy behind tuning, including curation, labeling, synthetic data generation, deduplication, and protecting against leakage between training and evaluation data\nBuild the platform\nOwn the roadmap for a shared model customization platform covering data preparation, training, evaluation, registry, deployment and lifecycle management\nPartner with infrastructure and engineering teams on GPU capacity, training and serving stacks, inference optimization, and cost controls\nMake tuned models reproducible and supportable, with versioned data, configs and checkpoints, clear ownership, and rollback\nPartner with vertical teams to launch\nWork with business and domain teams to scope use cases, pick models, design tuning experiments and launch to production\nTurn early wins into repeatable patterns, templates and services that the next team can reuse\nRun model launch reviews covering quality, safety, cost, performance and operational readiness\nMeasure and improve\nDefine the evaluations every model must pass before launch: task-specific quality, regression against the base model, safety, robustness and cost and latency targets\nPartner with the Agent Intelligence team so tuned models are measured in production and feedback flows back into better data and better models\nReport the impact of customization in terms leaders care about, such as quality gained, cost saved, latency reduced and risk avoided\nGovern models responsibly\nSet standards for license review, model provenance, data rights, model documentation, red-teaming and approval to deploy\nMake sure confidential and customer data used in tuning is handled within Equinix’s security, privacy and regulatory requirements\nLead across the organization\nBe Equinix’s go-to product leader on model selection and customization, and influence teams you don’t manage\nWrite the strategy, specs and executive briefs that align engineering, data science, infrastructure, security and business leaders\nIn your first year, you will\nPublish Equinix’s model selection framework and recommended-model catalogue, backed by benchmarks on our own workloads\nLaunch a shared customization platform that takes a team from data to a deployed, evaluated, tuned model\nShip tuned open-weight models into production with at least two vertical teams, with measured gains over the baseline\nEstablish a governance process for licensing, data rights and approval to deploy that teams can move through quickly\nQualifications\n7+ years across product management, engineering or applied ML, including 3+ years as a product manager on AI/ML, data or infrastructure platforms\nHands-on experience customizing and shipping open-weight models in production, such as fine-tuning, parameter-efficient tuning, preference optimization, distillation or quantization\nDeep practical knowledge of prompt and context engineering and retrieval-augmented generation, and sound judgment about when to use them instead of training\nExperience selecting models for real applications, weighing quality, cost, latency, hardware and licensing\nStrong evaluation skills, including building task-specific evals, avoiding train/test leakage, and telling a real improvement from noise\nWorking 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\nComfort reading and writing Python, running experiments, and reviewing training and evaluation results yourself\nAbility to lead without authority across engineering, science, infrastructure and business teams, and to explain trade-offs clearly to senior leaders\nNice to have\nExperience with reinforcement learning from human or AI feedback, or with reward modeling\nExperience with synthetic data generation and data curation at scale\nExperience with tool-use or agentic fine-tuning, or with function-calling models\nExperience with open-weight model licensing, model governance or responsible AI programs\nExperience running models in private, hybrid or on-premises environments, or with data residency requirements\nExperience in regulated industries such as financial services\nA degree in computer science, engineering, machine learning or another quantitative field, or equivalent experience. Publications or open-source contributions are a plus\nWhat this role is not\nThis 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.\nWhat success looks like\nEvery team knows which model to start with and why, and model choices are backed by Equinix’s own benchmarks\nTeams reach for fine-tuning only when it pays off, and when they do, the platform makes it fast and repeatable\nTuned models in production beat their baselines on quality, cost or latency, and the gains are measured and reported\nModel launches pass a clear, quick governance process with no surprises on licensing, data rights or safety\nEquinix builds lasting expertise in running and improving its own models\nWhy this role matters\nChoosing 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.\nThe targeted pay range for this position in the following location is / locations are:\nUnited States - Redwood City Office GHQ : 190,000 - 286,000 USD / Annual\nUnited States - Dallas Infomart Office DAI : 158,000 - 238,000 USD / Annual\nCanada - Toronto Office TRO : 158,000 - 236,000 CAD / Annual\nOur pay ranges reflect the minimum and maximum target for new hire pay for the full-time position determined by role, level, and location.The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job-related skills, experience, and relevant education and/or training.\nThe targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits. Employees are eligible for bonus, and equity may be offered depending on the position.\nEquinix Benefits\nAs an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.\nEmployee Assistance Program: An Employee Assistance program is available to all employees.\n\nUS Benefits: - Insurance: You may enroll in health, life, disability and voluntary plans that are designed for you and your eligible family members. - Retirement: You and Equinix may contribute to a retirement plan to help you plan for your financial future. - Paid Time Off (PTO) and Paid Holidays: You will receive an accrued amount of PTO each pay period along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to change and may be subject to specific plan or program terms.\n\nCanada Core Benefits: - Insurance: You may enroll in healthcare coverage that is designed to complement the provincial healthcare system, along with life, disability and optional benefit plans that are designed for you and your eligible family members. - Retirement: You may also enroll in Equinix-sponsored retirement or savings plans: Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax-Free Savings Plan (TSFA). - Vacation and Paid Holidays: Equinix offers both vacation and personal time, along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to specific plan or program terms, and to change at Equinix discretion.\nEquinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.\nEquinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.\nWe use artificial intelligence in our hiring process. 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