AI Developer Educator Jobs

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Design computational material science problems to challenge a frontier AI model that must have an answer verifiable by code and require a specialized tool like ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others. Pick an anchor tool and design a problem based on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or community-validated data pipelines. Write a Python reference solution, supply input files and model or domain definitions where needed. Decide the numerical answer and determine the domain-appropriate tolerance to count as correct. Test the problem against the model in batches of parallel attempts, tuning the difficulty until the agent only succeeds in a small number of attempts. Once finalized, the task is reviewed by a senior reviewer for quality feedback. Calibrate the problem by tuning it against batches of agent runs to reach a pass rate between 10–30%, involving rewriting waveform scenarios, tightening inversion parameters and solver tolerances, and monitoring agent behavior. Gain deeper command of the anchor tool and develop an intuition for how the frontier model navigates complex seismic, oceanographic, and sub-surface flow problems.

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Design computational engineering problems to challenge a frontier AI model, ensuring each problem has an answer verifiable by code and requires a specialized tool such as Cantera, CoolProp, CalculiX, OpenFAST, or others. Produce problems that run inside a sealed Linux container with the pre-installed tool and a programmatic judge that grades the model's answer. Select an anchor tool and design a problem focusing on its solvers, simulation kernels, or domain-specific models. Write Python reference solutions, supply input files and geometry or mechanism definitions as needed. Determine the numerical answer and set a domain-appropriate tolerance for correctness. Test and tune the problem against batches of parallel model attempts until the agent's success rate falls within the 10-30% range. Submit the completed task for senior reviewer feedback to ensure high task quality. Calibrate problems by rewriting thermodynamic cycles, adjusting material models and boundary conditions, and monitoring AI agent behaviors to refine problem difficulty. Acquire deeper command of the anchor tool and develop practical intuition for how the AI model handles complex thermal, structural, and fluid mechanics challenges.

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Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics including industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work such as market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task, which are used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.

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Contributors may design original optics problems that simulate real physics research workflows, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics, base problems on real research challenges or practical applications from optics and physics practice, and document problem statements clearly providing verified correct answers.

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Contributors may design original optics problems that simulate real physics research workflows, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. They develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics, based on real research challenges or practical applications from optics and physics practice. They also document problem statements clearly and provide verified correct answers.

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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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Own the developer-facing narrative for the model library by creating guides, tutorials, demos, and reference content to help developers select the right model for their needs. Build and nurture both sides of the model ecosystem by onboarding new models and educating developers. Develop evaluation frameworks and use-case-specific criteria beyond benchmark scores to reflect real-world developer requirements. Create community programs, events, and educational initiatives to establish Baseten as the main destination for model discovery and selection. Collaborate cross-functionally with product, engineering, and marketing teams to translate developer feedback into product improvements. Represent Baseten externally at conferences, meetups, and community events.

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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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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.