Member of Technical Staff, AI Engineering

About the job

Responsibilities

• Design and build systems to improve the quality, scale, and safety of data for frontier model training, including synthetic data generation and PII removal.
• Translate research, partner, or compliance needs into hypotheses, experiments, evaluation plans, and production-quality implementations.
• Build and enhance data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems.
• Run iteration loops involving prototyping, evaluation, interpretation of results, and system/product development.
• Partner with researchers, domain experts, and legal/compliance teams to ensure data utility and responsible handling.
• Identify and productize repeatable patterns into reusable software and platforms.
• Raise the technical bar through design judgment, communication, code quality, and mentorship.

Requirements

• 2-10 years of experience in synthetic/LLM-generated data, post-training/model-evaluation, privacy engineering, or data anonymization at scale.
• Proven track record as a hands-on individual contributor.
• Strong Python skills for writing clean, efficient, scalable software for large datasets.
• Sound judgment for data quality, risk, and utility, including forming hypotheses, choosing metrics, and diagnosing failures.
• Experience designing systems, considering tradeoffs in quality, scale, reliability, and reuse.
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership.
• Collaborative communication skills and ability to work effectively with diverse teams.

Benefits

• Work on problems at the center of frontier AI system improvement.
• Shape the roadmap, standards, and culture of an early technical organization.
• Move fluidly from research insight to real-world systems.
• Build durable infrastructure for careers in the AI economy.