AI Trainer Jobs

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Conduct structured process interviews with leads across Finance, Legal, HR, Operations, and Business / Revenue to map recurring, high-cognitive-load tasks. Identify which tasks are suitable for Claude skill encoding — well-defined inputs, predictable structure, clear success criteria. Write, test, and iterate Claude skills: system prompts, instructions, behavioral rules, few-shot examples, output formats, and edge case handling. Validate each skill directly with end users until it performs reliably for non-technical team members. Maintain a documented skill library with scope, usage guide, known limitations, and update log for each skill. Build and share a reusable methodology for skill writing that other teams can apply independently after the internship.

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Develop high-quality documentation, tutorials, and sample code to enable developers to effectively understand and utilize Mistral AI's products and integrations. Contribute to the community by resolving issues, providing answers, and offering guidance to users, fostering a supportive and collaborative environment. Lead developer relations initiatives and develop programs to engage the community. Organize and participate in community events centered around Mistral AI solutions. Work closely with the AI community to develop and maintain integrations, ensuring seamless compatibility and optimal performance. Remain informed about industry trends, best practices, and emerging technologies in AI, continuously updating your knowledge and skills.

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Contributors may design graduate- and industry-level automotive 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 graduate- and industry-level automotive 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 graduate- and industry-level automotive 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 graduate- and industry-level automotive 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 graduate- and industry-level automotive 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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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. Apply structured scoring criteria to assess multi-step problem solving.

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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. Apply structured scoring criteria to multi-step problems.

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Using experience with LMMS, the candidate will create tool-related questions and review AI-generated responses for accuracy and relevance to real-world music production and beat-making workflows. The role involves evaluating AI-generated content and providing feedback to help AI better understand music tasks and creative production workflows. The work is flexible, hourly contract-based, and can be done alongside other activities, with no minimum hour requirement.