AI Jobs in Berlin

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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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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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Lead complex AI-driven deployments in production, owning technical delivery across multiple deployments from scoping high-impact Agentic AI use cases to stable production. Apply technical expertise and problem-solving skills to design solution architectures, develop decision logic, deploy production-grade Generative AI agents, and align with key customer stakeholders, ensuring an outstanding experience and rapid time to value. Scope work effectively, sequence delivery, proactively remove blockers, and make trade-offs between scope, speed, and quality for successful and timely project delivery. Partner with product management to convert customer needs into actionable insights that influence the product roadmap. Develop reusable resources, best practices, and tools to scale the forward deployed engineering function across the organization.

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As a Lead Forward Deployed Engineer, you lead complex AI-driven deployments in production, owning technical delivery across multiple deployments from scoping AI use cases to stable production. You apply technical expertise, problem-solving skills, and creativity to help organizations address challenges by designing solution architectures, developing decision logic, deploying production-grade Generative AI agents, and aligning with customer stakeholders while ensuring rapid value for customers. You scope work, sequence delivery, remove blockers, and make trade-offs between scope, speed, and quality to ensure project success. You partner with product management to translate customer needs into product insights influencing the product roadmap. You develop reusable resources, best practices, and tools to scale the engineering function and actively coach and mentor junior engineers.