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What are AI training jobs? A guide for PhDs and master's graduates. 07/18/2026 What is an AI training job? AI training jobs involve helping improve artificial intelligence systems by providing human feedback on model outputs. Modern AI systems are trained on enormous amounts of data, but they continue to rely on human evaluation throughout their development. Companies use AI trainers to assess the quality of responses, identify factual errors, evaluate reasoning, test new capabilities, and generate high-quality training data. This feedback helps developers refine models, improve performance, and better align AI systems with human expectations. Depending on the project, AI training work may include: * Comparing multiple AI-generated responses and ranking which one is better. * Evaluating responses for factual accuracy, reasoning, clarity, or completeness. * Writing prompts that test an AI model's capabilities. * Revising or rewriting responses to create higher-quality examples. * Reviewing outputs within a specialized field such as medicine, law, software engineering, finance, biology, or education. * Identifying unsafe, misleading, or biased responses. The specific responsibilities vary by company and project, but the common goal is the same: providing structured human feedback that helps improve the quality, reliability, and usefulness of AI systems. If you're interested in exploring opportunities to contribute to AI development, After Your PhD partners with Mercor and Handshake to share AI-related job postings and opportunities. These roles can vary widely depending on the company, project, and area of expertise, but many involve using your academic background to help train, evaluate, and improve AI systems. What does an AI trainer actually do? The exact responsibilities depend on the company and the project, but most AI training work falls into a few common categories. One of the most common tasks is evaluating AI-generated responses. You may be presented with two answers to the same question and asked which one is better, then explain your reasoning using a detailed rubric. Your feedback helps companies understand what makes a response useful, accurate, and easy to understand. Other projects focus on writing prompts that challenge AI systems with difficult or realistic scenarios. These prompts help expose weaknesses in the model's reasoning and create better training data for future improvements. Some assignments involve fact-checking AI-generated content, identifying hallucinations, correcting technical mistakes, or rewriting weak responses so the model has a stronger example to learn from. For people with specialized expertise, the work becomes even more interesting. A biologist might evaluate scientific explanations, a lawyer could review legal reasoning, an economist may analyze financial responses, and a software engineer might assess AI-generated code. In these projects, you're applying your professional knowledge to improve how AI performs within your discipline. The benefits of AI training jobs. Flexible work That fits around your schedule. One of the biggest reasons AI training has become popular among academics is its flexibility and earning potential. Most projects are completed remotely, and many allow you to choose when you work. That makes these opportunities especially attractive if you're finishing your dissertation, teaching as an adjunct, interviewing for industry positions, or simply looking for additional income between jobs. While every person's experience is different, I've found that flexibility is one of the benefits people mention most often. Unlike many traditional part-time jobs, AI training often allows you to work when it's convenient for you instead of committing to fixed shifts. You continue using your academic skills. Many academics worry that leaving higher education means leaving behind years of specialized training. AI training is one of the few opportunities where that's not necessarily true. Rather than stepping away from research, writing, analysis, and critical thinking, you're applying those same skills in a completely different setting. Evaluating evidence, explaining your reasoning, identifying errors, and communicating clearly are all valuable parts of many AI training projects. One benefit I didn't fully appreciate until talking with people working in AI training is how much they learned about AI itself. After spending weeks or months evaluating model outputs, many people said they became significantly better at using tools like ChatGPT in their own work. You begin to understand where AI excels, where it struggles, and how better prompts often lead to dramatically better results. Whether you're interested in research, consulting, teaching, or simply becoming a more effective AI user, that's a valuable skill to develop. Your expertise can lead to higher-paying opportunities. Not every AI training project pays the same. Generalist projects typically pay less than projects requiring specialized expertise. If you have experience in medicine, law, software engineering, finance, education, science, or another technical discipline, you may qualify for projects that pay significantly more than entry-level opportunities. While compensation varies considerably by company and project, subject-matter expertise is often one of the biggest factors influencing pay. The downsides. Like any type of freelance work, AI training isn't perfect. AI is also changing the job market. One reality that's difficult to ignore is that many of the same advances creating AI training jobs are also changing the broader job market. As AI systems become more capable, some tasks that were previously performed by humans are becoming automated, and organizations are rethinking how certain types of work are completed. Exactly how AI will affect employment over the long term is still uncertain, and the impact is likely to vary widely by industry and occupation. Rather than replacing entire professions overnight, AI is often changing specific tasks within existing roles. For many PhDs, this creates an interesting trade-off. By working in AI training, you're contributing to the development of technologies that may reshape parts of the labor market, while also gaining firsthand experience with the tools that are driving those changes. Whether you view that as an opportunity, a concern, or a combination of both is ultimately a personal decision. Income can be unpredictable. Perhaps the biggest downside is that most AI training work is project-based. Projects can begin and end with little notice, available hours may fluctuate, and there may be periods where little work is available. Because of this, I generally encourage people to think of AI training as supplemental income rather than something to rely on as their only source of financial stability. Not every project will be exciting. It's also important to have realistic expectations. AI training work isn't always intellectually stimulating. Some projects involve evaluating hundreds of similar responses or following detailed rubrics for long periods of time. That repetition can become mentally draining, especially if you're used to the variety and independence that comes with academic research. On the other hand, not every project feels this way. Many people enjoy assignments that closely align with their area of expertise, particularly when they're evaluating technical content or solving complex problems. Like most freelance work, the experience often depends on the specific project you're assigned. Not necessarily. While software engineering projects certainly exist, many AI training opportunities don't require programming experience at all. Companies are looking for experts across dozens of disciplines, including biology, chemistry, education, psychology, history, law, medicine, business, finance, writing, and languages. In many cases, your ability to evaluate information critically is far more valuable than your ability to write code. One unexpected benefit of working in AI training is that you begin to understand how large language models think, where they excel, and where they still struggle. If you're interested in learning more about artificial intelligence, these books are excellent places to start. A practical guide to using AI effectively in your everyday work. This is one of my favorite introductory books for professionals who want to understand tools like ChatGPT without diving into technical machine learning concepts. An accessible look at how artificial intelligence is changing work, business, and society. Rather than focusing on the technology itself, the book explores the broader implications of increasingly capable AI systems. If you're interested in understanding how modern AI systems are built and deployed, this book provides an excellent overview of large language models, evaluation, and production AI systems. A fascinating exploration of how researchers are trying to ensure AI systems behave the way humans intend. Topics like reinforcement learning from human feedback (RLHF), bias, and model evaluation connect directly to many AI training jobs. A balanced introduction to what AI can - and can't - do today. This is an excellent starting point if you're completely new to artificial intelligence. Ryan Collins PhD is an SEO Strategist at Go Fish Digital. Ryan completed his PhD in Media Arts and Sciences at Indiana University Bloomington in 2021. During his time at Indiana University, Ryan eventually pivoted into a career in SEO and Digital Marketing after having informational interviews with working professionals in SEO, working on side projects, and gaining industry experience.

Mercor eyes $20B - read the AI 'revenue' number first. Mercor is raising at a $20B valuation on a $2B revenue run-rate. Here's how to read AI vendor numbers before you bet your stack on them. An AI company most of your customers have never heard of is about to be worth $20 billion. Mercor - a marketplace that supplies human experts to train and evaluate the frontier models everyone else builds on - is in talks to raise at a $20 billion valuation, roughly double where it sat nine months ago. The number worth studying isn't the valuation. It's the "$2 billion in revenue" underneath it. What actually happened. Per TechCrunch, Mercor is in early talks for a new round at a $20 billion valuation - up from $10 billion when it raised $350 million in October 2025 - and has reportedly already received a term sheet. CEO Brendan Foody says the company crossed $2 billion in annualized revenue in June, a 100% jump in four months. Alongside the raise, Mercor said it's acquiring Deeptune, an AI-agent-training startup whose entire team is joining. Forbes and Bloomberg reported the same valuation talks the same day. Now read the revenue line carefully. "$2 billion annualized" is a run-rate - a recent month multiplied out, not $2B booked over a year. And Mercor's model is a marketplace: it connects thousands of contract experts to AI labs that need training data and evaluations, which means a large share of that top line is pass-through - money that flows straight to the people doing the work, not margin Mercor keeps. (The exact take-rate isn't disclosed.) None of that makes Mercor a bad business. It makes the headline something other than what "$2 billion in revenue" sounds like. Why it matters for your business. You're going to be sold on numbers like these. Every AI vendor pitching you - the model provider, the agent platform, the tool your SaaS just bolted on - will wave a valuation and a run-rate as proof they're safe to build on. Those aren't the same claim. A doubling valuation on an annualized top line, in a business where most of the money passes through to contractors, tells you the category is hot. It tells you very little about whether that vendor will still exist, at that price, in three years. Its rule when Rushcommerce help a client pick an AI vendor: ignore the headline, read the unit economics. Ask what the revenue number actually measures - booked or run-rate, gross or net - where the margin lives, and what happens to your workflow if the vendor gets acquired, repriced, or runs out of runway. Mercor's rise makes a quieter point too: the "intelligence" in the models you rent rests on an army of paid human experts you never see. That's the real cost structure of AI, and it's worth remembering the next time a demo makes it look like magic. Key takeaways * Mercor is in talks to raise at a $20B valuation - double its $10B mark from October 2025 - and says it hit a $2B annualized revenue run-rate in June * "$2B revenue" is a run-rate on a marketplace top line; much of it is pass-through to the contract experts who do the labeling and evaluation, not margin * Headline valuation and run-rate are hype signals, not durability signals - judge AI vendors on unit economics and on what happens to you if they're acquired or repriced * Frontier models run on human expert labor you don't see - that's the actual cost base of the AI you rent Picking AI vendors to build on? Rushcommerce help small teams choose tools by durability and real economics, not funding-round theater - and Rushcommerce build systems you own, so a repricing is a memo, not an emergency. See how Rushcommerce work or get a vendor-durability review. * #mercor * #ai-funding * #vendor-risk * #ai-economics * #data-labeling Tommy Rush - Founder, Rush Commerce Operator turned builder. 15+ years running operations - now shipping the systems businesses run on. More Get The Rush Report weekly - one email, zero fluff.