AI Engineer

About the Position

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.

Responsibilities

• 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 vibes.
• Run models in production: self-hosted inference, latency, cost, and what happens when a provider has a bad day.
• Keep the agents safe: guardrails on what an agent is allowed to do, and one customer's data never in another's context.
• Work with our threat analysts to turn how a senior analyst reasons into something an agent can do.

Requirements

Must Have

• Python and TypeScript in production. One very well, the other well enough to ship in it.
• You know how LLMs actually work: attention, context windows, tokenization, sampling, why they hallucinate and what you can do about it.
• You have built LLM-based systems that real users depend on, not only demos: agents, tool-using pipelines, or something we haven't thought of.
• Software engineering fundamentals. An agent is a distributed system that sometimes improvises: retries, timeouts, idempotency and observability still apply.
• An evaluation mindset. You have built evals before, and you trust a number over a good-looking transcript.
• Strong on tests. You test the deterministic parts like any other software, and you have a strategy for the parts that aren't.
• You have dropped an approach because the evals said so, and you can tell us what you learned.

Nice to Have

We use these every day. You'll learn the ones you don't know yet.

• Hands-on fine-tuning: LoRA/QLoRA, PEFT, Unsloth, dataset curation.
• Serving open-weight models: vLLM or similar, quantization.
• MCP and multi-agent orchestration.
• Classical ML, when an LLM is the wrong tool.

What We Offer

• A competitive salary: €45k–70k gross a year (RAL), depending on your level. The range is indicative: if you are truly exceptional, we'll talk numbers above it.
• Full-time, permanent contract, CCNL Commercio, with health insurance.
• Full remote from anywhere in Italy. We do about 95% of the work remotely; the rest is alignment days and company retreats.
• A MacBook Pro. It's what we all work on.
• An AI subscription for everyone. Use it, a lot.
• Flexible hours, holidays managed on trust, zero micro-management.
• Growth budget: time for learning, courses and certifications, internal tech talks, and conferences paid for, even more so if you are the one on stage.
• Meal vouchers.

How to Apply

There is no "send us your CV" form here. To apply, you have to hack the site.

The application form is behind a short challenge: a few puzzles that a curious developer solves with the browser devtools, a terminal and some patience. It's the same for every role. Solve it, then send us your CV and your GitHub profile. If you like the challenge, you'll probably like the job.

What Happens Next

1. An intro call with your future manager and our Product Manager: who we are, who you are, what you are looking for.
2. One or two technical interviews with the engineers you would work with.
3. A final interview with our CTO, and then an offer.

About the Company

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.