What the role involves
A big part of Prox is AI agents that process complex technical documents into structured knowledge. The agents are right most of the time. When they're wrong, we need you to catch it.
You'll work inside a review platform we built. Each task shows you the source material, what the agent produced, and the steps it took to get there. You compare them and grade the agent's work.
what you'll juggle
- Read the source and the agent's output side by side. Verify the content was captured accurately.
- Review what the agent did. What it created, changed, or left out.
- Score a short rubric covering accuracy, coverage, organization, and rule adherence. Full rubric provided at onboarding.
- Write detailed feedback about the mistake. This is the most important thing you produce since we use it to improve the agent.
- Submit. Move to the next task.
conditions:
- Subject matter shifts over time. You don't need prior knowledge of the subjects. You need to be able to compare two documents carefully and spot where they disagree.
- Rate is fixed for the engagement. If it changes, it goes up, and we tell you before your next task.
- Work product owned by Prox (work-for-hire).
- Standard NDA at offer stage.
skills required:
- Strong written English
- Can read dense technical content for hours without losing focus
- Consistent scoring and clear, specific feedback
- Reliable on committed hours
preferred:
- Prior AI trainer/evaluator experience (Outlier, DataAnnotation, xAI, Surge, Mercor, Invisible, Toloka)
- Technical writing, editing, QA, translation, paralegal, or research background
About Prox
Every product ever sold needs support at some point. That support falls into one of two buckets. Bucket 1: Simple stuff. T-shirts, screen protectors, keyboards. You buy it, it shows up, maybe you ask “where's my order?” once. This is solved. Zendesk and a hundred other horizontal companies solved it. Bucket 2: Hard stuff. $20,000 industrial heaters. HVAC systems. CNC machines. Car parts. Products where buying wrong means your building doesn't have heat or your manufacturing line is down. Support for these products can only be performed by highly trained domain specialists and there aren't enough of them. If you're selling EV charging stations, your support person needs to be a certified electrician who understands local power grids, installation codes, and compatibility matrices. You can't hire this off the street. You can't outsource it overseas. You'd think LLMs would have solved this by now. They haven't. Three years into the LLM era, penetration in this industry is very low & the reason is twofold. First, off-the-shelf models don't actually understand these products. The knowledge lives in 48-page technical manuals buried on some manufacturer's website in terrible formatting — wiring schematics, compatibility matrices, installation diagrams that can only make sense visually. A general-purpose LLM can't draw you the diagram showing how to connect terminal A to terminal B. It doesn't have the spatial understanding or the product-specific reasoning to be a real technical advisor. So companies still rely entirely on human experts. Second, even if the models were good enough, there are no harnesses to make them useful in the business. No engine to capture deep technical knowledge about complex physical products and keep it updated. No way for a company to offload tribal knowledge from their senior technicians into a system. No way to see what questions customers are actually asking and feed that back into the knowledge base. No generative multimodal presentation and no expressive voice support. Prox is building the best technical product expert for extremely complicated physical products. A multimodal agent that can draw wiring schemes, share CAD models, process incoming videos from a technician in the field, and support people over the phone with voice that can pass the Turing test. To get there, we're solving multimodal knowledge graph building at a very deep level. A huge portion of your work will be developing SOTA knowledge engines that can truly understand complex physical products.
Full Prox profile