As an AI Engineer, Software Development Life Cycle, you build and improve the systems that allow AI to generate and evolve software.

Your goal is to make AI-driven development reliable, fast, and scalable.

Responsibilities

Your work includes:

• building and improving our context and harness for coding agents
• developing prompts, skills, and automation workflows
• designing development pipelines and tooling
• creating evaluation frameworks
• testing new models and capabilities

You continuously experiment and iterate to improve automation performance. We want to use the best coding agents on the market and make them build the Pit way.

What success looks like

• Coding agents reliably generate high-quality systems
• Automation significantly reduces development time
• Improvements in models translate into real productivity gains

Qualifications

Required

• 5+ years of professional software engineering experience spent building production systems in TypeScript/Node.js
• All-in on coding agents since at least Claude Opus 4.1 (August 2025). You should be using them daily and have strong intuition about what works and what does not
• Experience building harnesses, skills, or tooling around coding agents to improve their reliability and output quality
• Actively using more than one coding agent (Claude Code, Codex, Gemini, or others) and able to compare their strengths and weaknesses
• Demonstrated ability to iterate quickly, run experiments, and translate results into production improvements
• Clear, direct communication style. You write well and explain technical decisions to non-technical stakeholders
• Authorised to work in Sweden, willing to approve a background check

Preferred

• Experience building evaluation frameworks or benchmarks to measure AI system performance
• Deep understanding of software development lifecycle practices, including testing, CI/CD, code review, and deployment
• Background in developer tooling, internal platforms, or infrastructure that other engineers depend on

Not a fit if

• You use coding agents casually but have not thought deeply about how to make them reliable and consistent at scale
• You are primarily a data scientist or ML researcher looking for a model training role
• You get attached to particular solutions or approaches. The state of the art moves fast and we need people who follow the research, run experiments, and go where the data leads rather than defending what worked last month

About Pit

Where intent becomes reliable systems.

Our vision is a world where the friction between an idea and a production-ready system is zero. Our mission is to help companies run their operations on custom software they can trust.

Our principles

1. Start with the customer. Everything we build serves someone. We stay close to real users and workflows to understand their constraints and outcomes, testing against reality rather than our own taste. The customer is not a stakeholder to satisfy. They are the reason we exist.

2. Think like an owner. We take extreme ownership of our work and our mistakes. We flag problems early, make a plan, and focus on the solution. Silence is not neutrality; it is a choice that lets problems compound.

3. Set the standard. Quality compounds, but so does mediocrity. We hold exceptionally high standards in our product, code, design, and hiring. We will take shortcuts, but conscious ones.

4. Build lego blocks, not artworks. Everything we ship should compound. Every decision we document should make the company smarter.

5. Win together. No one carries the weight alone. Act with good intent. Assume your colleagues are trying their best.

6. Empower people with technology. Every solution we ship should make organizations and the people in them more capable, not just more efficient.