Agent Harness Engineer

Viktor; Warszawa; Remote; PLN 524,000 - 786,000; Full time

Benefits offered by this job

Remote work in Europe

Occasional in-person team meetups

Job summary

Viktor seeks an experienced engineer to build and evolve an AI agent harness that can operate with AGI-scale models. You will shape prompts, memory, and tool integrations to enable reliable autonomous work across diverse tasks. You will implement evals, drive improvements, and ship features to users via Slack and Teams.

The role offers a fast, small-team environment with direct access to founders, and opportunities to influence the product at a frontier of AI tooling.

Qualifications

• Built or significantly modified an AI agent harness.
• Tuned system prompts, skills and tools for complex problems.
• Developed custom skills, commands or servers to speed coding.

Responsibilities

• Build the agent runtime: loop for planning, actions, and verification.
• Engineer the context: shape system prompt, memory, and tools interplay.
• Make the agent fluent with tools across integrations.
• Grow agent capabilities: memory and self-written skills.
• Implement evals and catch regressions; fix what's broken.
• Ship product: reach users through Slack and Teams.
• Collaborate in a small team with broad scope.

Skills

Agent harnessing

Python

Eval infrastructure

Tool integration

Memory design

Tools

Custom CLIs

MCP servers

Agent tooling

Job description

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 56,000+ 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.

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.