AI Jobs in Munich

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As a Data Science AI Trainer at Mindrift, you will design original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. Your responsibilities include developing problems that require non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction, while ensuring deterministic problems with reproducible answers by avoiding stochastic elements or requiring fixed random seeds. You will base problems on real business challenges including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. Additionally, you will design end-to-end problems covering the complete data science pipeline from data ingestion, cleaning, exploratory data analysis, modeling, validation, to deployment considerations, and incorporate big data processing scenarios requiring scalable computational approaches. Verification of solutions using Python with standard data science libraries and statistical methods, as well as clear documentation of problem statements with realistic business contexts and verified correct answers, are also part of your duties.

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Create functional black box tests for large codebases in various source languages, create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms, monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards, and leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Design original computational STEM problems that simulate real scientific workflows, create problems that require Python programming to solve, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains and creative problem-solving approaches, verify solutions using Python with standard libraries (numpy, pandas, scipy, sklearn), document problem statements clearly and provide verified correct answers.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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Design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; apply structured scoring criteria to multi-step problems.

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Create challenging coding test cases to push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases. Craft challenges that are fair but hard, where the AI has all the context it needs, requiring complex reasoning with information scattered across files and external sources. Analyze AI failures to understand the model's struggles and strengths. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.

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Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases. Write comprehensive functional tests that validate actual end-to-end behavior and edge cases, craft fair but hard challenges requiring complex reasoning and scattered information, analyze AI failures to understand model strengths and weaknesses, and iterate based on feedback from expert QA reviewers who score work on seven quality criteria.

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Contributors design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may design rigorous electrical engineering problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.