We are hiring for a fast‑growing European AI scale-up that has quickly gained significant visibility in the market. Their platform is already used by major European enterprise customers to deploy and continuously improve advanced AI-driven solutions.In this role, you will:

• Design and build Python-based tools and pipelines to automate speech-to-text benchmarking and evaluation workflows.
• Improve STT performance on complex use cases such as names, dictation, and email-related speech scenarios.
• Work closely with the language capability team on language factory initiatives, including tooling that accelerates high-quality data generation.
• Apply LLMs and information retrieval techniques to production-oriented language systems and evaluation frameworks.
• Contribute to scalable, reliable engineering systems across distributed workflows, real-time data flows, and Azure-based infrastructure.

You will thrive if:

• You have strong Python engineering experience and a track record of building practical systems in AI-augmented development environments.
• You have hands-on experience with LLMs and information retrieval, ideally including deploying or supporting LLMs in production.
• You are comfortable using AI coding assistants and agent-driven workflows for debugging, refactoring, test generation, and delivery acceleration.
• You have great communication skills with technical staff and business stakeholders as well as proactive way of working!

Nice-to-have:

• You bring exposure to speech technology, STT, speech processing, or adjacent audio-focused systems.
• You understand linguistic variation across languages and scripts and can apply that insight to technical product and model improvements.

This role focuses on language factory initiatives spanning automated STT benchmarking, performance improvement for difficult speech scenarios, and tooling to speed up data generation. The engineering environment is deeply AI-native, with Python-centered development across distributed, event-driven systems on Azure and strong emphasis on observability, reliability, and operational scale.

The work would start ASAP, and continue until further notice.