Kotlin Desktop Engineer

WhyHireWrong?•Capon Bridge, WV

1d•PLN 252,000 - PLN 373,000•Remote

About The Position

This role is for a European software engineering company with a culture focused on ownership, clean code, and transparency. They are building a desktop control plane for local AI inference within a small, fast-paced, startup-like team. The team has an internal MVP, engages directly with potential users, and is moving towards open-sourcing the tool, all with minimal bureaucracy. The engineer will build a desktop application using Kotlin and Compose Desktop to deploy, configure, and manage AI models on local hardware, including NVIDIA Spark and DGX Spark. This is a native desktop control plane, not a web app with a wrapper, responsible for model lifecycle, agent configuration, system load visualization, and LLM proxy routing. The role involves creating UI for dashboards and configuration screens, integrating with backend REST APIs, implementing configurations in TOML, JSON, and YAML, building LLM proxy routing, and packaging the application into native installers for Windows, Linux, and macOS. The engineer will own features end-to-end, work on novel problems, navigate OS-specific challenges, and design unique solutions in a collaborative, small-team environment.

Requirements

Strong Kotlin skills.
Experience developing and maintaining build systems such as Gradle, Make, or Bazel.
Experience building or maintaining developer tooling (e.g., IDE plugins, CI/CD tooling, internal workflow automation, review tooling).
Solid command of Kotlin Coroutines and async programming.
Practical understanding of differences between Windows, macOS, and Linux at the desktop application level.
Preference for low-level, in-the-weeds technical work.
Ability to drive a feature independently.
Fluent English.

Nice To Haves

Open source or DX-focused project experience.

Responsibilities

Build a desktop application in Kotlin and Compose Desktop.
Deploy, configure, and manage AI models on local hardware.
Create the UI for model management dashboards and agent configuration screens.
Integrate with backend REST APIs for model deployment.
Implement configurations using TOML, JSON, and YAML.
Build LLM proxy routing to connect coding agents to locally hosted models.
Package the application into native installers for Windows (MSI), Linux (DEB), and macOS (DMG).
Own features end to end.
Work on problems that do not have Stack Overflow answers.
Navigate OS-specific quirks.
Design solutions that have not been built this way before.
Collaborate closely with a small team through code review.