Member of Technical Staff - Data Flywheel Infra, Frontier 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.

Required

• Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience.
• Software Engineering experience using Python,SQL, Spark/Flink/Ray
  
  Preferred
  
  
• Experience building AI training-data governance platforms, including provenance, licensing/rights metadata, consent management, PII handling, policy enforcement, or auditable lineage.
• Experience managing third-party datasets, data partnerships, licensed content, or externally sourced data with complex contractual and usage restrictions.
• Experience building privacy- and security-aware systems for first-party product or user data, including isolation, access controls, retention/deletion, and purpose limitation.
• Experience with data clean rooms, privacy-preserving processing, de-identification, confidential computing, or secure data collaboration.
• Experience building evaluation → failure mining → data generation → training feedback loops.
• Experience with synthetic data, model graders, reward signals, hard-example mining, active learning, or data-mixture optimization.
• Experience with multimodal or agentic datasets including text, image, video, audio, web, GUI, tool-use, or interaction trajectories.
• Understanding of Modern LLM training workflows including Pre-training, SFT, RL/post-training, evaluation, and synthetic data.
• Strong understanding of data governance, security, privacy, provenance, access control, and data lifecycle management.

Data Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Data Engineering IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.