Member of Technical Staff Data Flywheel Infra

What you'll do

• Build scalable infrastructure for ingesting, processing, and serving first-party and third-party data for model pre-training and post-training.
• Integrate automated governance, security, and compliance features including PII detection, usage rights, and audit workflows into the data platform.
• Develop automated pipelines for data filtering, deduplication, quality scoring, and semantic enrichment to construct high-quality datasets.
• Create feedback loops that convert model failures and evaluation signals into actionable tasks for targeted data acquisition and improvement.
• Build synthetic and AI-native data pipelines using LLMs and agents to automate labeling, validation, and transformation.
• Develop metrics and infrastructure to monitor dataset quality, diversity, contamination, and policy compliance.
• Maintain clear provenance between human-created, third-party, and model-generated data while enforcing usage constraints.

What we're looking for

• A Master's degree in a relevant field and 4+ years of experience in data science, software development, or related fields.
• A Bachelor's degree in a relevant field and 6+ years of experience in data science, software development, or related fields.
• Equivalent professional experience to the specified degree and years of experience requirements.
• Software engineering experience using Python, SQL, Spark, Flink, or Ray.
• Experience building AI training-data governance platforms including provenance, licensing, PII handling, and policy enforcement.
• Experience managing third-party datasets with complex contractual restrictions and privacy-aware systems for first-party data.
• Experience building evaluation-to-failure mining feedback loops and working with synthetic data or model graders.
• Understanding of modern LLM training workflows including pre-training, SFT, RL, and multimodal dataset management.