Copy Editor for AI/ML Technical Content - Humanize and Polish AI-Generated Drafts

# Copy Editor for AI/ML Technical Content - Humanize and Polish AI-Generated Drafts We are looking for a strong copy editor to help polish a series of AI/ML technical marketing articles. We will provide content briefs, AI-generated first drafts, and technical review notes from AI/ML subject-matter experts. Your role is to turn those drafts into clear, credible, human-sounding content that technical readers will actually want to read. This is not generic SEO rewriting. We need someone who can improve flow, clarity, tone, and readability while preserving technical accuracy. ## Topics Covered The content series is about LLM fine-tuning and related AI engineering topics, including: - What is fine-tuning? - RAG vs. fine-tuning - How to fine-tune an LLM - LoRA vs. QLoRA vs. full fine-tuning - Fine-tuning evals - How to prepare fine-tuning data - The real cost of fine-tuning - Best LLM fine-tuning platforms - Enterprise buyer's guide to LLM fine-tuning - How to train a custom AI model without ML expertise You do not need to be a machine learning researcher, but you should be comfortable editing technical B2B content and preserving technical nuance. ## What We Need From You For each draft, we need you to: - Make the writing sound more human, direct, and credible - Remove generic AI-written texture - Improve openings, transitions, and conclusions - Tighten long or repetitive sections - Make complex technical ideas easier to follow - Preserve the meaning of technical claims - Keep the article grounded and avoid hype - Improve readability for technical founders, AI engineers, ML engineers, product leaders, and enterprise AI buyers - Flag places where the copy is unclear, unsupported, or too vague - Preserve citations, technical review notes, and factual caveats The goal is not to make the content fluffy or overly simple. The goal is to make it sharp, useful, and believable. ## What "Humanize" Means Here Good humanizing: - Replaces generic claims with specific, plain-language explanations - Removes filler and repetitive phrasing - Makes the piece sound like it was written by a thoughtful operator, not a content mill - Preserves technical nuance and uncertainty - Adds better structure where the draft rambles - Improves rhythm and readability - Makes the argument easier to trust Bad humanizing: - Adds unsupported claims - Makes technical content vague - Overuses metaphors or hype - Removes important caveats - Turns the piece into sales copy - Changes the technical meaning - Adds fake examples or fake authority ## Ideal Background You may be a strong fit if you have experience editing: - AI/ML content - developer tools content - technical B2B SaaS content - cloud infrastructure content - data/analytics content - cybersecurity or fintech content - technical founder thought leadership - long-form SEO content for expert audiences Bonus if you have edited content for AI startups, developer platforms, infrastructure companies, or enterprise software companies. ## Deliverables For each article, deliver: 1. A polished Google Doc or Markdown draft. 2. Tracked changes or clear comments where helpful. 3. A short note summarizing the biggest edits made. 4. Any remaining concerns about clarity, claims, tone, or structure. 5. A list of sections that still need technical review, if any. ## Screening Process Shortlisted candidates may be asked to complete a short paid editing test or join a live call. In the call, we may walk through a short AI-generated technical paragraph and ask how you would improve it. We are not looking for someone who simply makes copy sound more polished. We are looking for someone who can recognize when writing sounds generic, inflated, or untrustworthy, and can make it clearer without weakening the technical substance. Candidates who can explain their edits clearly, preserve technical nuance, and avoid adding unsupported claims will be the strongest fit. ## Example of the Editing Style We Want Before: "Fine-tuning is a powerful way to unlock the full potential of large language models by adapting them to your organization's unique needs and enabling enhanced performance across a wide variety of business use cases." Better: "Fine-tuning is useful when you need a model to perform a repeated task in a specific way. It is not the answer to every AI problem. If the issue is missing knowledge, retrieval may be better. If the issue is inconsistent behavior on a task you can measure, fine-tuning may be worth testing." Before: "Enterprises can leverage fine-tuning to achieve better outcomes, lower costs, and improved AI performance." Better: "Fine-tuning is easiest to justify when the task is frequent, measurable, and expensive to run through a frontier model every time." ## Engagement Structure We would like to start with 1-2 paid trial edits. If the work is strong, we have a larger batch of AI/ML fine-tuning articles ready for editing. Please include in your application: - Relevant editing experience - Links to writing or editing samples - Experience with technical B2B or AI content - Your preferred rate per article or hourly rate - A brief note on how you approach editing AI-generated drafts - One example of a sentence or paragraph you have improved, if available ## Important We care about credibility more than hype. These articles should not sound like AI-generated SEO content. They should sound like clear, experienced, technically careful writing for readers who can spot exaggeration quickly. Your job is to make the content more readable, more human, and more trustworthy without breaking the technical meaning.