AI Trainer Jobs

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

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Contributors 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; and apply structured scoring criteria to multi-step problems.

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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 libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn; improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

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

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Contributors may 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; and apply structured scoring criteria to multi-step problems.

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Use experience with LMMS to create tool-related questions and review AI-generated responses for accuracy and relevance to real-world music production and beat-making workflows, evaluating AI-generated content and providing feedback to help AI better understand music tasks and creative production workflows.

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Write the playbook for onboarding and educating customers onto Sierra’s Agent SDK and Agent Studio no-code platform. Lead the creation and execution of developer training and enablement programs. Build scalable content including technical documentation, educational videos, onboarding paths, and sample repositories for SDK adoption. Develop and deploy AI Agents as an Agent Software Engineer to build expertise in agent development. Collaborate with marketing teams to publish developer case studies and success stories to inspire and connect the technical community.

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Write the playbook for onboarding and educating customers onto Sierra, including the Agent SDK and Agent Studio no-code platform. Lead the creation and execution of developer training and enablement programs. Build scalable content such as technical documentation, educational videos, onboarding paths, and sample repositories for SDK adoption. Develop and deploy AI Agents as an Agent Software Engineer, building expertise in agent development. Collaborate with marketing teams to publish developer case studies and success stories to inspire and connect the technical community.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help customers achieve success. Scope and co-develop production-level data science projects with customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.