TPM, AI Data Infra & Systems

Microsoft AI•Redmond, WA

37d•$165,600 - $331,200

About The Position

At Microsoft AI, we build the frontier models and products that reach millions of people, from pretraining through post-training, evaluation, and launch. Our Data Acquisitions, Operations, and Governance squad is a centralized team embedded deeply with research and product stakeholders, running a large and fast-growing portfolio of supplier relationships, first-party data programs, and licensing deals. For MAI, the operational system behind that portfolio has to be as rigorous as the research it feeds. We are looking for a Principal Technical Program Management to lead Data Acquisition Operations, with hands-on engineering and technical program management experience to support AI data operations at MAI. You'll partner closely with research, product, compliance, and infra bringing together process and engineering that turn manual, fragmented workflows into a reliable, automated, auditable supply chain for training data.

Requirements

Extensive experience leading operations at scale in data operations, supply chain, vendor and supplier management, or procurement operations.
Experience managing engineering teams and owning a technical roadmap, with the judgment to make build, buy, and partner decisions and the credibility to lead engineers directly.
A track record of replacing manual, spreadsheet-driven processes with real systems with experience in owning and iterating on enterprise-wide platforms.
Technical fluency in data pipelines, ETL, SQL/Kusto, data warehouses, databases, postgres DB, NoSQL DB, ingestion and validation, cloud storage and access control, and metadata and lineage, with the ability to reason about trade-offs between coverage, metadata completeness, and operational simplicity.
Experience operating where data governance, privacy, licensing, and regulatory and audit obligations and disclosures are first-order constraints.
Strong executive communication and the ability to drive alignment across research, engineering, finance, legal, infrastructure, and external suppliers.
Familiarity with the AI/ML data landscape, including pre-training corpora, multimodal data, post-training and RL task supply, and the supplier ecosystem.

Responsibilities

Own the data acquisition operational and governance model end-to-end, establishing a single source of truth across opportunities, contracts, work orders, delivery tracking, inventory, and budget for both acquired and cross-company data.
Manage and grow the operations, automation, and engineering team, including direct management of technical leads and an engineering function owning core acquisition infrastructure.
Own the acquisition platform roadmap and its delivery: automated ingestion and validation, supplier-facing delivery and metadata capture, publishing pipelines, role-based reporting that separates financial views from researcher discovery, and persistent lineage across datasets and model training runs.
Partner with compliance, infrastructure, and platform leadership on data governance, defining strategy for review gates, access control models, retention and disclosure obligations, and clear ownership boundaries between acquisition systems and central services.
Lead supplier operations at scale, covering onboarding, standardized work orders and delivery requirements, quality and throughput measurement, and contractual quality protections, including tying tracking and metadata completeness to payment readiness.
Reduce procurement and supplier onboarding cycle time against explicit targets, and instrument the pipeline so throughput, coverage, and data quality are measured rather than inferred.
Sponsor the strategic approach of audit, compliance, and regulatory requirements end-to-end, as governance owners convert them into tracked, owned engineering and process work.
Build the universal researcher-facing data catalog so that acquired data is discoverable, well-described, and trustworthy at the point of use.
Eventually, address frontier issues in data quality, quality assurance, and other areas related to data usage and quality.