AI Technical Writer

Chameleon Integrated Services•St. Louis, MO

3d•Onsite

About The Position

Chameleon Integrated Services is currently looking for an AI Technical Writer to support one of our clients in downtown St. Louis. This is a 6-month contract with the possibility of renewal. Technical writers are the power behind performant large language models (LLMs). We are looking for writers with exceptional critical reading and technical writing abilities to oversee the production of written content that trains cutting-edge AI models for defense use cases. While this role involves military subject matter, prior military service is not required. We value your ability to research, understand, and synthesize complex technical information above all else.

Requirements

Proven experience in technical writing, copywriting, or academic writing, with a portfolio demonstrating the ability to explain complex concepts clearly.
Demonstrated ability to quickly digest and understand technical, government, or legal documents.
Meticulous eye for grammar, syntax, and style consistency.
No military experience is required for this role, provided you have strong research capabilities.
6-8 years of professional writing experience

Nice To Haves

Experience writing for government, legal, or engineering sectors.
Familiarity with DoD writing styles or government style guides is a plus.
Background in Political Science, International Relations, History, or English.

Responsibilities

Research & Synthesis: Analyze complex source materials related to intelligence, operations, logistics, and other defense disciplines to create high-quality training data.
Critical Evaluation: Apply strong critical reading skills to evaluate and critique military- style documents written by and for AI systems, ensuring accuracy, tone, and logical consistency.
Content Creation: Write clear, concise, and structured responses to a variety of prompts regarding government and defense topics, adhering to specific style guides.
Best Practices: Help define best practices for improving LLMs and assist in creating guidelines for Model Evaluation and Reinforcement Learning from Human Feedback.