Axio Education is an AI-native education technology company that unifies learning, engagement, and operations through a single adaptive platform. The role involves applied AI research focused on building agentic LLM systems that incorporate various advanced AI techniques and methodologies.
Responsibilities
- You’ll work on applied AI research building agentic LLM systems that combine planning, tool use, memory, and multi-step reasoning, with additional emphasis on graph-based reasoning, knowledge graphs, representation learning, and (where useful) GNNs
- This is a hands-on role: you’ll design experiments, implement and iterate on agent workflows (strong preference for JavaScript/TypeScript), read and apply recent research papers, and evaluate reliability, cost, and real-world performance
Skills
- Strong understanding of ML fundamentals
- Strong knowledge of transformers / LLMs
- Hands-on experience building agentic systems (planning + tool use + memory + evaluation)
- Strong programming ability in JavaScript / TypeScript (Python ok, but JS/TS preferred)
- Understanding of knowledge graphs and structured representations
- Familiarity with representation learning
- Experience or academic exposure to GNNs
- Ability to read and apply research papers independently
- Self-driven, responsible, and comfortable working with minimal supervision
- Shipped or open-sourced an agent framework / agentic product in JS/TS
- Experience with RAG, long-term memory, retrieval + reranking, or graph-based retrieval
- Experience with evaluation: agent benchmarks, ablations, reliability testing, cost/perf tradeoffs
- Experience with PyTorch, JAX, or training/fine-tuning workflows
- Multi-agent systems, tool orchestration, function-calling/tool APIs, or workflow engines
- Prior research experience (academic or industry), publications, or strong technical writing
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