NET CHECK GmbH was founded in 1999 with the aim of improving the quality of communication networks. Since then, NET CHECK has developed into the leading partner of network operators and infrastructure providers of mobile and fixed networks of all technologies. Its core competencies include international network benchmarking (comparative measurements) as well as network planning and fault analysis. NET CHECK has one of Germany's largest crowdsourcing platforms, which generates over 144 million data points every day. Our commitment to quality and security has earned us the trust of scientific and government institutions.
NET CHECK is headquartered in Berlin and is part of the NC GROUP, an owner-managed group of companies with a total of over 180 employees at five locations in Germany and one in Belgrade, Serbia.
Job Summary: We build AI platforms that transform complex telecom network data—including drive tests, voice quality, broadband diagnostics, and signaling analysis—into clear, actionable insights.
We are looking for an Applied AI Scientist who combines strong analytical thinking with practical engineering skills. You will model complex problems, develop and prototype ML and agentic AI solutions, and turn them into reliable, production-ready tools for real users while applying the latest developments in AI.
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Design and build agentic AI systems and RAG pipelines that can reason over technical documentation and live network data.
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Take ownership of testing and evaluation by developing evaluation datasets, metrics, and benchmarks for accuracy, retrieval quality, latency, and cost. Ensure system quality can be measured reliably and regressions are identified before reaching users.
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Develop and continuously improve our evaluation and observability practices, including offline and online evaluations, LLM-as-a-judge approaches where appropriate, regression testing, and quality dashboards for agentic AI and RAG systems.
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Turn data science insights into production-ready solutions, covering the full process from data analysis and modeling to well-tested, maintainable AI tools, including operationalization and MLOps.
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Develop and enhance machine learning models for classification, anomaly detection, root-cause analysis, and related use cases, and integrate them into the platform.
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Write clean, well-tested, and maintainable code, actively contributing to high engineering standards across the team.
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Research and experiment with new AI models, methods, and technologies, turning promising approaches into practical features and production solutions.
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Work closely with engineers, data specialists, and domain experts to develop effective solutions for real-world telecom challenges.
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PhD in a quantitative discipline such as Physics, Mathematics, Computer Science, Engineering, Computational Chemistry/Biology, or a related natural science, with strong hands-on experience in mathematical and computational modeling.
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Minimum 3 years of experience developing software or machine learning systems in an industry or research environment, alongside or following your studies.
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Hands-on experience with AI agents and RAG, including building and implementing LLM-powered systems in practice.
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Strong background in data science and machine learning, with experience using tools and frameworks such as scikit-learn, XGBoost/LightGBM, and deep learning frameworks.
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Strong software engineering and programming skills, particularly in Python, with a focus on clean, well-structured, tested, and maintainable code.
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Good understanding of the design and development of agentic AI systems, including orchestration, tools, memory, evaluation, and guardrails.
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A strong research mindset and enthusiasm for keeping up with developments in AI and translating new approaches into practical solutions.
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Fluent English, combined with strong communication, collaboration, and teamwork skills.
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Experience productionizing ML solutions in the cloud, preferably AWS (e.g. Bedrock, ECS, S3), using modern AI and LLM tooling such as LangGraph, Chroma, FAISS, LangSmith, or Langfuse, including LLM evaluation, observability, prompt engineering, and cost and latency optimization is a plus.
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Exposure to telecom, networking, signal processing, or other data-rich technical domains is a plus.
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Employment Type: Full-Time, indefinite term.
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Opportunity to shape AI strategy in a fast-growing organization with access to state-of-the-art AI tools, infrastructure, and research opportunities
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Flexible working hours.
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28 vacation days.
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Health & Sports Subsidy: Sports membership subsidy.
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Public Transportation Subsidy: Monthly subsidy for a Germany-wide ticket.
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Bicycle leasing via JobRad.
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Regular Team Events.
