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Prox builds AI technical support for complex physical products (think power tools, powersports equipment, agriculture, heavy machinery -- anything that requires an installation guide or complex manuals).
backed by Y Combinator, Bloomberg Beta, Paul Graham, SV Angel, Burst Capital and many more.
We run the company out of a git-based knowledge graph. Every customer meeting note. Every investor and vendor conversation. Our writing style, our voice, icp, our positioning. Product ideas, platform new features and roadmap decisions. Marketing campaigns, LinkedIn content, outreach sequences, conference pipeline.
Structured, linked, versioned, with an agent layer on top that reads, writes, syncs, and briefs and completes tasks. This is what we build for our customers: take everything a company knows about its products -- manuals, specs, forum threads, support history -- and make it queryable by an AI that never forgets and never sleeps. We do the exact same thing for ourselves.
The person we're looking for has a specific character flaw: they cannot do the same thing twice without immediately wanting to eliminate the second time it happens. Not from the laziness, but because repetition feels like a design failure.
They think like a hacker. Every system has a seam, and if you find it, you can usually make 10x the impact with a fraction of the effort. They don't ask permission to automate. They notice the pattern and close it.
But they have taste. Output that looks like AI slop -- bothers them. Be it an email, customer proposal, blog posts or event announcement. The aesthetic of the work matters a lot as we're entering .
Your job is to be the judgment layer on top of everything the agents can't decide -- and to keep extending the system so the list of things agents can't decide gets shorter.
(disclaimer: most of this work exists to eventually eliminate itself except the last two)
Bonus : prompt engineering experience
Application → founder call → take-home challenge → paid work trial → offer
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.
Salary
$135,000 - $135,000
Equity
0.35% - 0.35%
Location
San Francisco, CA
