AI Engineer · July 28, 2026

The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra

video AI Engineering Model Evaluation Prompt Engineering

The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra video thumbnail

Why it matters

At Palantir, forward deployed started as a literal description: you were deployed, physically, at the customer's site, and the onboarding project was keeping the platform from falling over. Natalie Meurer's dirty secret is that the title never settled after that.

My takeaway: The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra is an enterprise-adoption signal. The practical read is to watch how deployment scale, data boundaries, operational ownership, and platform controls change as AI moves out of experiments.

Keep exploring

More curated notes connected through AI Engineering and Model Evaluation.

OpenAI News · framework

OpenAI’s Frontier Governance Framework

OpenAI's 22-page Frontier Governance Framework maps its frontier-model processes to California's Transparency in Frontier AI Act and the EU AI Act's general-purpose AI code. It documents lifecycle risk assessment, cyber-offense and other risk tiers, mitigation and residual-risk decisions, critical-incident handling, security risk management, model reporting, external review, responsibility allocation, and change control.

OpenAI News · framework

Cybersecurity in the Intelligence Age

OpenAI proposes a five-pillar strategy for AI-enabled cyber defense: tiered access for trusted defenders, faster government-industry coordination, stronger protection of frontier models and infrastructure, risk-scaled deployment monitoring, and broader defensive support for individuals and small organizations.

OECD.AI Wonk · guide

A five-step roadmap to closing the AI evaluation gap

The roadmap addresses evaluation results that overstate real-world performance or fail to transfer across deployment contexts. Its five steps balance standardized and local tests, evaluate throughout the lifecycle, build qualified assurance and communication capacity, tailor tests to each value-chain actor and technology, and use a coordinated, trusted process for updating methods.