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

Wand Synthesis AI Inc

16d

About The Position

Wand turns AI into labor, enabling humans and AI agents to operate together as a unified, hybrid workforce with comprehensive management and oversight. 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. 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. We're hiring a Staff Machine Learning Engineer to join University, a brand new team focused on agent memory, evolution, and reasoning. For at least the next six months, the focus will be on teaching agents to reason and remember without retraining, deploying through the cloud and focusing on high-leverage activities rather than model training, fine-tuning, or deep GPU/CUDA work. This role has one of the more senior technical bars in this hiring round and offers significant opportunity for impact.

Requirements

• Shipped production agents or agent-adjacent systems in a company setting, not just in a lab.
• Experience with memory, context engineering, or techniques that improve agent reasoning without retraining.
• An applied, builder's mindset with a focus on rapid iteration (shipping in days and weeks).
• Comfortable owning ambiguous, senior-level problems independently.
• Strong software engineering fundamentals complementing ML and agent experience.
• Practical fluency with the modern agent tooling stack, including vector databases (Pinecone, Weaviate, pgvector, or similar), retrieval frameworks (LangChain, LlamaIndex), and agent orchestration tools (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 tools (e.g., LangSmith, Ragas, TruLens, or a custom eval harness).
• Strong communication skills, both written and verbal.

Nice To Haves

• An advanced degree (MS or PhD) combined with industry experience.
• Experience testing and benchmarking agent behavior.
• Experience building 'skills' or reusable capabilities for AI agents.
• Experience with agents handling significant volumes of complex information.
• Experience in a fast-scaling product and engineering organization.
• Experience with large data volumes.

Responsibilities

• Build agent memory systems, including mechanisms for generating, curating, refining, and storing information.
• Design memory systems with constraints for confidentiality and scoping to prevent agent leaks.
• Build systems to monitor agent behavior across the organization and translate it into scalable, shared best practices.
• Develop reusable 'skills' for agents, enhancing reasoning, decision-making, and report writing capabilities.
• Design and execute tests and benchmarks to validate the effectiveness of agent improvements.
• Contribute to shaping the technical roadmap for agent memory and reasoning as the team establishes itself.
• Take undefined problems and design practical, shippable solutions.
• Clearly document methodologies to facilitate knowledge sharing and future development.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior