Applied AI Engineer, Learning Intelligence
About The Position
We are building the intelligence layer that powers how learners grow. This role sits at the intersection of machine learning, knowledge representation, and product engineering. You will own the skill and concept graph that defines what learners know and can do, infer skill gaps from behavioral and profile signals, and translate those inferences into personalized recommendations and dynamic learning that guide each learner to their next best step. You will also be the bridge between our AI capabilities and the engineers building our frontend, making sure AI-driven features ship in a way that is explainable, reliable, and production-ready.
Requirements
- 5+ years of experience in applied ML or data science, with production recommendation or personalization systems in your background
- Hands-on experience with knowledge graphs, graph databases, or ontology design
- Experience with LLM APIs and prompt engineering for generative features
- Hands-on history of shipping LLM-based systems to production, including large-scale deployment, evaluation frameworks, and agentic workflows
- Advanced Python proficiency and experience architecting robust, production-grade applications
- Deep familiarity with the modern AI stack, from retrieval and agent frameworks to complex prompt engineering, model evaluation, and context engineering
- A high degree of intellectual curiosity and the ability to find elegant, straightforward solutions
- Exceptional communication skills, with the ability to translate technical logic for varied stakeholders
Responsibilities
- Design, build, and maintain a skill and concept graph that maps relationships between skills, roles, domains, and learning content
- Develop ML models that infer learner skill levels from usage patterns, work output, assessments, and profile data (not just self-reported input)
- Build and iterate on recommendation systems that surface the next best module, suggest learning paths, and generate content dynamically
- Partner with frontend engineers to ensure AI outputs are consumed correctly, surfaced with appropriate context
- Define explainability standards for model outputs so users and stakeholders understand why a recommendation was made
- Collaborate with product and content teams to validate recommendation quality and close feedback loops
- Monitor model performance in production and own the evaluation framework for recommendation quality
Benefits
- annual performance bonus
- equity
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
