Senior Generative AI Application Engineer, Learning & Development (R5856)

Job Description:

The Senior AI Engineer, Hivemind Learning & Development is a hands-on individual contributor who will build the AI-powered products, content systems, and measurement capabilities that help engineers learn Hivemind faster and apply it successfully. Embedded within the Hivemind Learning & Development team, this role combines production AI engineering, developer experience, and technical education.

You will own an AI training coach that provides grounded, context-aware guidance; create tooling that turns learning objectives and product changes into high-quality workshops; and partner with autonomy experts to produce deeply technical curricula, labs, and assessments. The role works closely with Applications Engineering, Hivemind engineering and Customer Success.

Success is defined by trusted learning products that ship, shorter time to technical proficiency, faster and more consistent workshop development, content that stays current with Hivemind releases, and clear evidence that training improves customer capability and product adoption.

What you'll do:

• Develop an AI-powered training coach that helps people learn Hivemind concepts, products, and development workflows.
• Create highly technical Hivemind training materials and hands-on workshops for engineers and other technical learners.
• Build prompt-based tooling that generates workshops and supporting learning content from defined objectives, audiences, and source materials.
• Create LLM-based tooling that automates the ongoing upgrade and improvement of Hivemind training and educational materials.
• Build AI tools and systems that enable, streamline, and automate Hivemind Learning & Development workflows.

Required qualifications:

• Progressive experience building production software, AI-enabled products, developer tools, technical learning platforms, or comparable systems.
• Hands-on experience integrating large language models, generative AI APIs, retrieval-augmented generation, agents, prompt workflows, or AI-assisted automation into production or enterprise environments.
• Strong software engineering fundamentals, including API design, testing, observability, documentation, secure coding, maintainable architecture, and operational ownership.
• Professional proficiency in Python and the ability to read, debug, and create credible examples in modern C++.
• Experience translating complex software or systems concepts into clear technical documentation, hands-on labs, workshops, developer education, or enablement material.
• Ability to learn a sophisticated technical product quickly, work directly with subject-matter experts, and convert ambiguous needs into practical, testable learning solutions.
• Experience evaluating AI system quality using automated tests, structured human review, telemetry, or task-based performance measures.
• Clear written and verbal communication skills, sound technical judgment, and a collaborative style suited to engineers, instructors, customers, and cross-functional partners.

Preferred qualifications:

• Experience with autonomy, robotics, aerospace, defense, simulation, distributed systems, or safety-critical software.
• Experience with developer education, instructional design, learning science, intelligent tutoring systems, or assessment design for highly technical audiences.
• Familiarity with AI evaluation and observability, prompt and agent testing, vector databases, enterprise search, model gateways, and deployment in restricted or disconnected environments.
• Experience designing and facilitating hands-on technical workshops for customer engineers, including troubleshooting live technical environments.
• Bachelor's degree in Computer Science, Engineering, Robotics, Data Science, Learning Technology, or a related field, or equivalent practical experience.

Why this role matters:

Hivemind gives engineers a comprehensive platform to develop, test, and deploy AI pilots. The value of that platform depends on how quickly technical teams can build sound mental models, complete real workflows, and apply the software with confidence. This engineer will create the learning systems that make expert knowledge available at the moment of need, reduce the delay between product change and customer readiness, and help the Learning & Development team scale quality without losing technical rigor.