We are looking for a Backend / Post-Processing Engineer to join our engineering team.
You will work on the core processing pipeline that transforms raw test results, audio streams, signaling logs, and real-time metrics into meaningful reports, analytics, and alerts delivered to our customers.
Your work will directly impact the intelligence layer of our platform: reporting, analytics, monitoring, and audio-quality evaluation.
Tasks
* Design and implement backend logic for post-processing of test results and communication data
* Build & extend pipelines for advanced test analytics (voice quality metrics, audio analysis, MOS models, timing & signaling evaluation)
* Implement and optimize audio-engineering-related processing (signal analysis, codecs, waveform transforms, etc.)
* Work with infrastructure and deployment tools using Terraform and Docker
Requirements
* Python – primary language for data processing and analytics
* Go – high-performance backend services
* Perl – some legacy & core components (Mojolicious) you may interact with
* Terraform – infrastructure-as-code for our cloud environment
* Docker – containerized development and production deployment
* Experience with audio processing, telecom protocols (SIP, RTP), or test automation frameworks is a strong plus.
* Good communication skills in English; German is a plus.
* Ability to work in agile startup environments.
* AWS / Cloud-based environment experience
Benefits
* Work on cutting-edge automated testing and monitoring for real-time communication systems
* Shape the analytics and intelligence layer of a unique deep-tech product
* A modern tech stack with a fast-moving, engineering-focused
* Hybrid work model with flexibility. (Vienna/Graz/Home Office)
* Collaborative, friendly international team.
* Opportunities for deep learning in telecom, audio, and large-scale monitoring pipelines
Send us a message including
* a link to your LinkedIn profile or a CV, and…
* a summary of your accomplishments and/or a link to your Github
About the interview:
* Non-technical part (30min, video call)
* Technical part (1-2h, video call or on-site)
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