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

Build the Future Workforce

Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations.

Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.

Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.

Join Wand in leading the Agentic Shift

Wand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers.

You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern.
Position Summary

We're not fine tuning models. We're teaching agents to reason, remember, and get better without retraining a single one. We're hiring a Staff Machine Learning Engineer to join University, a brand new team we're standing up right now, focused on agent memory, evolution, and reasoning.

For at least the next six months: no model training, no fine tuning, no deep GPU or CUDA work. We deploy through the cloud and put our energy into something higher leverage instead. This is one of the more senior technical bars in this hiring round, and one of the most wide open.

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

• 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