Staff Machine Learning Engineer, Agent Memory & Reasoning

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Wand AI

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2022

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Wand AI is an enterprise software company (Wand Synthesis AI Inc. ) that builds the “Agentic Labor Infrastructure” to enable governments and large enterprises to create, manage, and scale hybrid workforces composed of humans and autonomous AI agents. Their platform (branded Wand OS / Agentic Workforce Technology) provides management, oversight, interoperability across systems, security options (SOC2-ready, on‑premise/private cloud/hosted), dashboards, decision tracking, and tools for deploying and governing agentic workflows at scale. Wand positions itself as a B2B/enterprise provider that turns AI into operational labor for regulated and large-scale organizations.

📋 Description

• 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.

🏖️ Benefits

• Health insurance • Paid time off • Flexible work arrangements • Professional development opportunities