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

Wand AI is a company that enables humans and AI agents to work together as a unified workforce, focusing on building the infrastructure for agent ecosystems. They are seeking a Staff Machine Learning Engineer to join a new team focused on agent memory, evolution, and reasoning, with responsibilities including building agent memory systems and shaping the technical roadmap.

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

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
• An advanced degree (MS or PhD), paired with real industry experience
• Experience testing and benchmarking agent behavior
• Experience building 'skills' or reusable capabilities for AI agents
• Experience with agents that handle serious volumes of complex information (think a genuinely capable assistant, not a demo)
• Time spent in a fast scaling product and engineering org
• Experience with large data volumes