Member of Technical Staff - Data Flywheel Infra, Frontier Models

We are looking for a Data Flywheel Infrastructure Engineer to build the infrastructure that continuously turns 1P data, 3P data, model signals, evaluation results, and synthetic data into high-quality training data for frontier LLM and multimodal models. This role owns the systems connecting: Data Acquisition → Governance & Compliance → Curation → Training → Evaluation → Failure Mining → Data Improvement A critical part of the role is enabling aggressive data iteration while ensuring that every dataset is secure, policy-compliant, rights-aware, traceable, and auditable . Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. This role is part of Microsoft AI's Superintelligence Team. The MAIST is a  startup-like team inside Microsoft AI , created to push the boundaries of AI toward  Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values.  Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being.   We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models!

Build 1P & 3P Data Flywheel Infrastructure Build scalable systems for ingesting, processing, curating, versioning, and serving first-party and third-party data for pre-training and post-training. Connect model failures, evaluations, and product signals back into targeted data acquisition, generation, and improvement workflows. Own Data Governance, Security & Compliance Infrastructure Build governance and policy enforcement directly into the data platform, including: Data provenance and lineage Usage rights, licensing, and consent metadata PII / sensitive-data detection and protection Access control and data isolation Retention and deletion enforcement Geographic and regulatory restrictions Dataset approval and audit workflows Training eligibility and purpose-based usage controls Build Policy-Aware Data Acquisition & Curation Systems Develop automated pipelines for 1P and 3P data ingestion, classification, filtering, deduplication, quality scoring, semantic enrichment, and dataset construction. Make governance policies machine-enforceable so that data can automatically be included, excluded, quarantined, or restricted based on its origin, license, sensitivity, consent, geography, and intended model use. Build Evaluation-to-Data Feedback Loops Convert model evaluations and real-world failure signals into actionable data tasks through failure clustering, hard-example mining, long-tail discovery, capability-gap detection, and targeted dataset generation. Enable rapid iteration from: Model Failure → Data Gap → Data Intervention → Training → Evaluation Build Synthetic & AI-Native Data Pipelines Use LLMs, VLMs, and Agents to automate data generation, labeling, filtering, quality validation, enrichment, and transformation. Maintain clear provenance between human-created, first-party, third-party, model-generated, and derived data , and enforce appropriate policies across each category. Build Data Quality, Attribution & Observability Develop metrics and infrastructure to measure dataset quality, coverage, diversity, contamination, duplication, policy compliance, and contribution to model capability improvements. Enable researchers to understand which data improves which capabilities and under what governance constraints . Qualifications