# Member of Technical Staff - Data Flywheel Infra, Frontier Models

**Company**: Microsoft AI
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: USD $142,800 – $274,800 per year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://microsoft.ai/job/member-of-technical-staff-data-flywheel-infra-frontier-models-2/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_06dfd0b0-8d8

## Description

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 role is part of Microsoft AI’s Superintelligence Team, created to push the boundaries of AI toward Humanist Superintelligence,ultra-capable systems that remain controllable, safety-aligned, and anchored to human values.

## Responsibilities

- Build 1P & 3P Data Flywheel Infrastructure: scalable systems for ingesting, processing, curating, versioning, and serving first-party and third-party data for pre-training and post-training.

- Own Data Governance, Security & Compliance Infrastructure: build governance and policy enforcement directly into the data platform.

- 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.

- Build Evaluation-to-Data Feedback Loops: convert model evaluations and real-world failure signals into actionable data tasks.

- Build Synthetic & AI-Native Data Pipelines: use LLMs, VLMs, and Agents to automate data generation, labeling, filtering, quality validation, enrichment, and transformation.

- 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.

## Qualifications

- 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 Qualifications

- 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.

## Salary Information

Data Engineering IC5 – The typical base pay range for this role across the U.S. is USD $142,800 – $274,800 per year. Data Engineering IC6 – The typical base pay range for this role across the U.S. is USD $165,600 – $296,400 per year.

## Skills

### Required
- Python
- SQL
- Spark
- Flink
- Ray
- Data Engineering
- Data Governance
- Security
- Compliance

### Nice to have
- third-party datasets
- data partnerships
- licensed content
- externally sourced data
- privacy-preserving processing
- de-identification
- confidential computing
- secure data collaboration
- synthetic data
- model graders
- reward signals
- hard-example mining
- active learning
- data-mixture optimization

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Source: [Apply at microsoft.ai](https://microsoft.ai/job/member-of-technical-staff-data-flywheel-infra-frontier-models-2/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)