Junior AI/ML Engineer, Data and Evaluation

RedMimicry GmbH · Berlinjunior

In short

▸Construyes datasets y evaluaciones para IA en seguridad cibernética con enfoque en calidad y reproducibilidad.
▸Trabajas con logs de seguridad, etiquetas precisas y pipelines automatizados para benchmarking y análisis de errores.
▸Destaca que tu trabajo determina si los avances de IA son reales y si los fallos se detectan antes del despliegue.

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What they ask for

✓Excelentes habilidades en Python
✓Experiencia con Pandas, NumPy, PyTorch o herramientas similares
✓Capacidad para procesar datos estructurados y no estructurados
✓Familiaridad con Git, pruebas automatizadas y flujos reproducibles
✓Entendimiento básico de evaluación supervisada y estadística
✓Interés en seguridad cibernética y análisis de amenazas

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PythonPandasNumPyPyTorchGitCIML evaluationbenchmarkingdata versioningexperiment tracking

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We are building a new capability that turns fragmented, noisy security logs into explainable, AI-powered threat analysis, delivered inside the RedMimicry platform. As an AI/ML Engineer at RedMimicry, you will build the datasets, benchmarks, and evaluation infrastructure behind our applied AI/ML work on security telemetry. The quality of this work depends on accurate ground truth, disciplined data handling, reproducible experiments, and systematic error analysis. Your work will determine whether reported improvements are real and whether regressions are caught before deployment. You will work closely with the Senior AI/ML Engineer and Offensive Security Engineer. This role is suitable for an early-career or intermediate engineer with strong programming and data skills who wants to work on applied AI in a technically demanding cybersecurity environment. This is a fixed-term position running until 31 October 2027. Tasks Curate Security Datasets: Prepare telemetry from controlled engagements and test environments. Build Ground Truth: Create and maintain labelled examples, annotation guidelines, and consistency checks. Maintain Evaluation Partitions: Separate training, validation, and test data along relevant dimensions. Automate Benchmarks: Implement metrics and maintain reproducible benchmark and regression pipelines that run locally and in CI. Run Experiments and Error Analysis: Evaluate models and methods, and identify recurring failure modes and data-quality issues. Maintain Data Quality: Implement schema checks, provenance tracking, deduplication, validation, and dataset versioning. Document Experiments: Produce clear experiment records, plots, tables, and technical summaries that support engineering decisions. Requirements You do not need to meet every requirement to apply. Strong practical work, research projects, open-source contributions, or a relevant thesis can compensate for limited commercial experience. Benefits Work from anywhere in Germany, and use our Berlin office as often as you like 30 days of paid time off, and a quiet inbox while you are away Company-paid Deutschlandticket Annual budget for the courses and certifications you pick yourself Modern tooling and extensive use of AI Light process, clear communication, focus on what really matters A close match is enough. If the role speaks to you, apply with your CV and anything else you would like us to see. What follows is short and transparent, a few conversations with the team and then your first week in Berlin. We are an equal opportunity employer and welcome applications from all backgrounds and genders. Questions about the role or the process are welcome at any point.

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