Backend Software Engineer // AI Music Video Platform

Requirements

• You've shipped production Python backends that handled real load - async workloads, queues, retries, the unglamorous stuff that makes systems actually work.
• You're comfortable working with AI APIs and have opinions about how to wrangle them: cost, latency, failure modes, model selection.
• You think in systems. When something breaks, your instinct is to find the root cause, not patch the symptom.
• You have high agency. You see a bottleneck, you fix it. You don't wait for a ticket.
• Bonus points: experience with video/audio processing pipelines (ffmpeg, GStreamer, etc.), GPU orchestration, multi-provider AI infrastructure, or distributed systems at scale.
• You're genuinely excited about generative AI and where it's heading. You probably have a side project or two that proves it.
• You're based in Berlin and can join us in the office on at least four days a week.

Benefits

Additional Information

Neural Frames is a leading AI music video generator, turning any song into a fully produced music video in minutes. We're bootstrapped, profitable, and growing fast at over $5M ARR. Our ambition is to build a category-defining company, helping tens of millions of audio creators to visualize their content and promote it online. As a profitable company, there is no VC drama. We are a small, ambitious group of people, striving to build something creators love. Tasks Own the systems that turn songs into videos: video generation pipelines, model orchestration, queue management, and the infrastructure that keeps it all running at scale. Design and build backend services in Python that orchestrate multiple AI providers (we work with fal, BytePlus, OpenRouter, AWS and others) and gracefully handle their quirks, failures, and pricing trade-offs. Improve reliability, latency, and cost across the generation stack. Every second shaved off a render and every cent saved per video compounds across millions of generations. Build the infrastructure for new product surfaces working closely with the founder and tech lead on architecture decisions. Instrument and monitor what you ship. You'll have real visibility into how your code performs in production and the autonomy to fix what's broken. Contribute to the AI/ML side of the stack: prompt engineering, model evaluation, fine-tuning pipelines, and experimenting with new models as they ship.

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