Applied AI Engineer - Remote

Overview

We're looking for someone at the cutting edge of autonomous coding, ensuring that our full-stack AI engineer is robust, reliable, and continually pushing the frontier of what’s possible. Your mission is to identify the biggest challenges impacting reliability - and come up with inventive, creative solutions. This isn’t just about making things work; it’s about making them unfailingly excellent and scalable.

In Short

• Change the world, literally: 2025 is the year AI works, and Lovable has the best AI full stack engineer product (According to industry veterans)
• Work, learn, have fun: We’re building the strongest technical team in Europe. You will shape our culture and be inspired in a place of exceptional talent density
• Build a unique network: Lovable is a global top AI brand that the world is eager to work with. You will meet and work with some of the most important people in tech
• Get autonomy and ownership: With lots of things being built, this is a unique opportunity to get ownership and have a true impact on our product
• Compensation: Lovable pays top of market cash and equity, and is committed to compensate better than any other company in Sweden on a performance basis

Requirements

• Full-Stack Proficiency: Experience working with full-stack applications, preferably using React on the frontend.
• Experience with LLMs: Practical experience working with LLMs and their quirks.
• Inventive Problem-Solving: You see challenges as puzzles waiting to be cracked, and you bring unconventional ideas to the table.
• Fast Debugger: You can quickly identify, analyze, and fix complex Applied AI issues.
• Bias for Action: You move fast, experiment, and iterate; shipping solutions is in your DNA.
• Curiosity for the Cutting Edge: You stay up-to-date with the latest research and apply it pragmatically.
• Strong Communication: Ability to clearly explain reliability challenges, solutions, and trade-offs to both technical and non-technical teammates.

Benefits

• Push the Limits: Identify bottlenecks and failure points in our code generation pipeline and devise ingenious ways to overcome them.
• Innovate and Iterate: Experiment with state-of-the-art reliability techniques for LLMs and autonomous agents.
• Rapid Debugging: Pinpoint and resolve complex reliability issues with speed and precision.
• Data-Driven Improvements: Analyze reliability patterns, run failure analyses, and implement strategies to boost accuracy and performance.
• Dogfood to the Max: Rigorously use our own product for internal development, pushing it to the limit in real-world scenarios, surface edge cases, break things (on purpose), and refine the product from the inside out.