TPM, AI Data Infra & Systems

Location: Mountain View, California, United States

Salary: USD $165,600–$296,400 per year across the U.S.; USD $220,800–$331,200 per year in the San Francisco Bay Area and New York City metropolitan area.

Company

Microsoft AI develops frontier AI models and products, supporting the full lifecycle from pretraining through post-training, evaluation, and launch.

What you will do

• Own the end-to-end operating and governance model for acquired and cross-company data, covering opportunities, contracts, work orders, delivery, inventory, budgets, and training usage.
• Lead and grow operations, automation, and engineering teams responsible for acquisition infrastructure and delivery.
• Own the platform roadmap for automated ingestion and validation, supplier metadata capture, publishing pipelines, reporting, and dataset-to-training-run lineage.
• Partner with compliance, infrastructure, and platform leaders on governance, access control, retention, disclosure, audit, and regulatory requirements.
• Scale supplier operations through standardized onboarding, delivery requirements, quality measurement, and contractual quality protections.
• Build a researcher-facing catalog that makes acquired data discoverable, well-described, and trustworthy.

Requirements

• Extensive experience leading scaled operations in data operations, supply chain, vendor or supplier management, or procurement operations.
• Experience managing engineering teams, owning technical roadmaps, and making build, buy, and partner decisions.
• Experience replacing manual, spreadsheet-driven workflows with enterprise systems and platforms.
• Technical fluency in data pipelines, ETL, SQL/Kusto, data warehouses, databases, PostgreSQL, NoSQL, ingestion, validation, cloud storage, access control, metadata, and lineage.
• Experience working with data governance, privacy, licensing, regulatory, and audit obligations.
• Strong executive communication and cross-functional alignment skills across research, engineering, finance, legal, infrastructure, and external suppliers.

Nice to have

• Familiarity with AI/ML data landscapes, including pretraining corpora, multimodal data, post-training, reinforcement-learning task supply, and supplier ecosystems.
• Experience addressing frontier data quality and quality-assurance challenges.

Culture & Benefits

• Collaboration with research, product, compliance, infrastructure, platform, finance, legal, and external supplier stakeholders.
• Benefits and other compensation may be available depending on role eligibility.
• Applications are accepted on an ongoing basis until the position is filled.