Agent Harness Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 187d ago
The date the source published, not the day we noticed it (2026-04-29). Last seen at its source 1h ago.
We have tracked this listing since 3 Sep 2026 (59 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
remote in Europe
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Warsaw, Munich, Europe
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay €60.2k–108.3k/yr
Middle 50% of 371 listings that do state pay — Engineering · all levels · Europe · EUR/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Ashby employer's own board first seen 59d ago · last seen 1h ago
The listing
The Short Version
You're the person who makes Viktor do more things, for more customers, more reliably. You've built agents before (runtime, tools, memory, evals) and have opinions about what makes them work. You ship the day you write the code and reach for agentic engineering by default. If you've never built an agent, this isn't the role.
The Hard Part
We're building a harness ready for AGI. Models keep getting better on their own; the harness decides how much of that intelligence turns into real work. Viktor shouldn't wait to be handed tasks. It should explore what a company is doing and automate it on its own.
Getting there is context engineering at the frontier: how the system prompt, skills, memory, and tools interact on diverse, complex problems, and how that interplay holds up across the real work of tens of thousands of workspaces.
What You'll Actually Do
Build the agent runtime: the loop where it plans, acts, and verifies its own work instead of declaring success it didn't earn.
Engineer the context: shape how the system prompt, skills, memory, and tools combine, and how the agent holds up when problems get diverse and complex.
Make the agent fluent with tools: composition across many integrations, recall, and the skill files that keep the right functions at hand.
Grow what the agent can do: memory it can trust, skills it writes for itself, work it runs on its own.
Make it measurably better: evals, catching regressions, and turning every failure into a fix.
Ship product: features that reach users through Slack and Teams within hours.
Whatever needs building. Small team, large surface.
Who You Are
You've personally built or significantly modified an AI agent harness, and can describe the trade-offs you made.
You understand how an agent's system prompt, skills, and tools interact on diverse and complex problems, and you've tuned that interplay yourself, not just read about it.
You've built custom skills, commands, CLIs, or MCP servers to make your own agentic coding faster.
Agentic coding is your daily workflow, and you've stress-tested models and harnesses across the frontier.
Systems thinking. You make hard technical trade-offs and design for how things break at scale.
Speed, with the bar up. You ship today, not Thursday.
Genuine interest in how AI works. You've read the papers and have opinions about evals.
Munich or Warsaw, or remote in Europe.
Why This Role Is Different
No layers. You work directly with both founders, and decisions get made in the room, not in a Linear ticket.
The agent is the product. Every reliability win shows up in retention the next week.
Frontier work. You're building a harness at the edge of what's currently possible, experimenting with agents that can take on full responsibilities, not just tasks.
Even Better If
Previous AI engineering experience: agents, harnesses, eval infrastructure.
Previous founder or early-stage builder.
Open source contributions to the AI tooling we live in.
Tech
Python. No agent framework, no orchestration library. We build the harness ourselves, from the loop up.
How we work
Small team, high trust, low process. Decisions are made by owners, not committees. You will ship your first week. You will talk to users your first day.
We don't do alignment meetings or stakeholder syncs. We build things, see if they work, and iterate.
Why Viktor
Singular focus: we're building a harness that can hold an AGI-level model and make it useful for everyone.
We're one of the fastest-growing companies in the world. The product works. The market is pulling.
This is a rare window: everyone here owns something real. Not a task. A surface of the company that customers depend on. That doesn't last forever. Right now, it's still true.
Compensation
Top-of-the-market salary and the kind of ownership that only exists at this stage.
The best work happens when you're in the room. This role is Munich or Warsaw, or remote in Europe, and we bring the whole team together in person a few times a year.