AI Engineer
RedCarbon builds AI agents for cybersecurity. Think of them as virtual colleagues that join a Security Operations Center and take on the work that burns analysts out: triaging the endless stream of alerts, investigating incidents, hunting for threats, writing the report at the end. The humans keep what needs a human: judgement, decisions, the call to the customer at 2am.
We are an Italian company with our HQ in Turin, remote since day one, and we closed a €3M Series A in 2025. Our customers trust us with their security, which is a polite way of saying that "it works on my machine" is not an option here.
We are a bit crazy. We put a hacking challenge in front of our application form, and a pixel-art mascot in our job posts. You are about to meet him.
Hi, I'm Rosso. Our release notes are written by an AI. This job post was too. Kidding. Mostly.
But we are dead serious about the engineering. We aim for a technical bar that is very, very high, and we are AI-first: we build AI products, and we build them with AI, every day, on purpose.
So we don't hire people to turn detailed requirements into code. We hire engineers who start from a problem, understand it, question the assumptions,
pick a reasonable solution, ship it, and check that it actually solved the problem. You don't need to know our whole stack on day one. You need solid fundamentals, and to learn fast.
The role
We're looking for an AI engineer who trusts a number over a good-looking transcript.
Our agents investigate security incidents for real customers. When an agent is wrong, an analyst wastes an hour, or misses an attack. As an AI Engineer you make sure that happens less every week: you build the harnesses our agents run in, you fine-tune the models they think with, and you prove with numbers that they got better.
What you'll do
- Build agent harnesses: tools, structured outputs, sub-agents, context management, caching and replay. The engineering that turns a model that sounds right into an agent that is right, run after run.
- Fine-tune open-weight models for focused security tasks, from dataset curation to a model serving in production.
- Write the evals. Benchmarks and regression suites that tell us whether a change made the agents better or just different. Nothing ships on
We are an Italian company with our HQ in Turin, remote since day one, and we closed a €3M Series A in 2025. Our customers trust us with their security, which is a polite way of saying that "it works on my machine" is not an option here.
We are a bit crazy. We put a hacking challenge in front of our application form, and a pixel-art mascot in our job posts. You are about to meet him.
Hi, I'm Rosso. Our release notes are written by an AI. This job post was too. Kidding. Mostly.
But we are dead serious about the engineering. We aim for a technical bar that is very, very high, and we are AI-first: we build AI products, and we build them with AI, every day, on purpose.
So we don't hire people to turn detailed requirements into code. We hire engineers who start from a problem, understand it, question the assumptions,
pick a reasonable solution, ship it, and check that it actually solved the problem. You don't need to know our whole stack on day one. You need solid fundamentals, and to learn fast.
The role
We're looking for an AI engineer who trusts a number over a good-looking transcript.
Our agents investigate security incidents for real customers. When an agent is wrong, an analyst wastes an hour, or misses an attack. As an AI Engineer you make sure that happens less every week: you build the harnesses our agents run in, you fine-tune the models they think with, and you prove with numbers that they got better.
What you'll do
- Build agent harnesses: tools, structured outputs, sub-agents, context management, caching and replay. The engineering that turns a model that sounds right into an agent that is right, run after run.
- Fine-tune open-weight models for focused security tasks, from dataset curation to a model serving in production.
- Write the evals. Benchmarks and regression suites that tell us whether a change made the agents better or just different. Nothing ships on