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EEpiminds
Stockholm, Sweden2 months ago
Work format
Onsite
English level
B2, upper-intermediate
An email or a Telegram message to the recruiter
We’re building Lucy, an AI system that does marketing work: investigating performance, finding opportunities, and carrying approved changes through to execution.
We’re a small team in Stockholm building the environment that lets agents do this work reliably across tools, data, and ongoing tasks.
A capable model still needs the right environment to do useful work. It needs access to tools, relevant context, a place to execute code, feedback on what happened, and clear boundaries for when to continue or stop.
Those choices compound. A tool response can overwhelm the context. A handoff can lose a constraint. A failed command can send an agent into a loop. An earlier assumption can become the basis for an unsupported action.
Our agents already delegate to specialists, run code in sandboxes, and retrieve client knowledge. The challenge is engineering the harness around them so they can complete longer, harder tasks with less intervention.
You’ll own the agent harness: the execution loop, tool interfaces, context assembly, memory, delegation, and feedback that turn model capability into dependable behavior.
You’ll work directly with the founders and engineers, moving between runtime code, traces, and evaluations. The backend engineer owns the underlying compute and durable execution infrastructure; you own how agents use that environment to get the job done.
The harness determines how much of a model’s capability reaches the user. You’ll own that layer at Epiminds, with room to rethink how agents work and direct responsibility for proving the result.
You’re a strong software engineer who has built and shipped systems using language models. You understand how tool design, context, and feedback affect agent behavior, and you can investigate failures across a full execution trace.
You’re comfortable with TypeScript and Python, designing experiments, and maintaining the runtime code behind them. Experience building agent frameworks, coding agents, execution environments, or evaluation systems is especially relevant.
Our stack includes TypeScript, Node.js, the Vercel AI SDK, multiple model providers, LangSmith, PostgreSQL, and sandboxed Python execution.
We’re building Lucy, an AI system that does marketing work: investigating performance, finding opportunities, and carrying approved changes through to execution.
We’re a small team in Stockholm building the environment that lets agents do this work reliably across tools, data, and ongoing tasks.
A capable model still needs the right environment to do useful work. It needs access to tools, relevant context, a place to execute code, feedback on what happened, and clear boundaries for when to continue or stop.
Those choices compound. A tool response can overwhelm the context. A handoff can lose a constraint. A failed command can send an agent into a loop. An earlier assumption can become the basis for an unsupported action.
Our agents already delegate to specialists, run code in sandboxes, and retrieve client knowledge. The challenge is engineering the harness around them so they can complete longer, harder tasks with less intervention.
You’ll own the agent harness: the execution loop, tool interfaces, context assembly, memory, delegation, and feedback that turn model capability into dependable behavior.
You’ll work directly with the founders and engineers, moving between runtime code, traces, and evaluations. The backend engineer owns the underlying compute and durable execution infrastructure; you own how agents use that environment to get the job done.
You’re a strong software engineer who has built and shipped systems using language models. You understand how tool design, context, and feedback affect agent behavior, and you can investigate failures across a full execution trace.
You’re comfortable with TypeScript and Python, designing experiments, and maintaining the runtime code behind them. Experience building agent frameworks, coding agents, execution environments, or evaluation systems is especially relevant.
Our stack includes TypeScript, Node.js, the Vercel AI SDK, multiple model providers, LangSmith, PostgreSQL, and sandboxed Python execution.
The harness determines how much of a model’s capability reaches the user. You’ll own that layer at Epiminds, with room to rethink how agents work and direct responsibility for proving the result.
We welcome applicants of all backgrounds.
We compare skills, grade, work format and salary
Most often 63,400 to 94,400
