Staff Machine Learning Engineer, Agent Memory & Reasoning (University)

Role Responsibilities

• Build agent memory systems: not just picking what goes into context, but the mechanisms that generate, curate, refine, and store that information in the first place.
• Design memory with real constraints: confidentiality and scoping so agents never leak what they shouldn't.
• Build systems that watch how agents behave across the org and turn that into shared best practices at scale.
• Build reusable "skills" agents can call on: better reasoning, better financial decisions, better report writing.
• Design and run tests and benchmarks that show whether these improvements actually work.
• Help shape the technical roadmap for agent memory and reasoning as the team stands up.
• Take an undefined problem and design a real, shippable solution for it.
• Document your methodology clearly enough that others can build on it.

Key Requirements

• You've shipped production agents or agent adjacent systems at a company, not just in a lab.
• Experience with memory, context engineering, or techniques that make agents reason better without retraining them.
• An applied, builder's mindset: rigorous thinking, shipped in days and weeks, not semesters.
• Comfortable owning ambiguous, senior level problems on your own.
• Strong software engineering fundamentals to go with your ML and agent experience.
• Practical fluency with the modern agent tooling stack: vector databases (Pinecone, Weaviate, pgvector, or similar), retrieval frameworks (LangChain, LlamaIndex), and agent orchestration tools such as LangGraph.
• Comfortable working directly with LLM provider APIs (OpenAI, Anthropic, or similar) and embedding models for retrieval and memory systems.
• Experience with agent evaluation and benchmarking tooling (e.g. LangSmith, Ragas, TruLens, or a custom eval harness).
• Strong communicator, written and verbal.

Preferred Experience