--- title: 'Staff Machine Learning Engineer, Agent Memory & Reasoning (University) at Wand Synthesis AI Inc' canonical: 'https://feeny.ai/job/staff-machine-learning-engineer-agent-memory-reasoning-university-wand-s0acgez56cp6' type: 'job' last_seen: '2026-09-07' --- # Staff Machine Learning Engineer, Agent Memory & Reasoning (University) at Wand Synthesis AI Inc - **Company:** Wand Synthesis AI Inc - **Location:** United States - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-07-26 - **Last confirmed live:** 2026-09-07 - **Apply:** https://jobs.ashbyhq.com/wand-ai/f5b44e9b-1be5-43e0-b03e-b94bc29360b6 ## Job description 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 ## About Wand Synthesis AI Inc ## Company Overview - **One-liner**: Wand AI builds the world’s first Agentic Labor Infrastructure, an operating system that enables governments and global enterprises to create, manage, and scale hybrid workforces where humans and AI agents collaborate seamlessly. - **Entity Type**: Private (Seed stage; $15M total funding) - **Headquarters**: Palo Alto, California, United States (also operates from Tel Aviv, Israel) - **Founded**: 2022 - **Founders**: Rotem Alaluf (CEO), Eli Osherovich (Co-Founder) ## Core Business - **Primary industry/industries**: Enterprise AI Infrastructure, Agentic Automation, Workforce Management - **Target customers**: Governments and global enterprises (B2B, Enterprise) - **Mission or purpose statement**: “To drive a generational leap for the global economy by integrating agent ecosystems into the core of work, business, and society — making this shift safe, transparent, and radically efficient.” ## Products & Services - **Agent Control Panel**: Configure, monitor, and govern every AI agent with live performance data, budget restrictions, and compliance controls. - **Collaboration Platform**: A shared chat workspace where humans prompt, collaborate with, and coach AI agents in real-time. - **Process Automation**: Agents run complex workflows end-to-end, triggering tasks and escalating edge cases to humans. - **Division Automation**: Hand an entire function (e.g., customer support, data operations) to AI when the business is ready, scaling from single processes to fully autonomous divisions. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed - **Key Metric**: $15.0M in total funding across 2 rounds - **Notable Investors/Partners**: InnoRevo Ventures (lead investor in seed round); backed by world-renowned entrepreneurs and leading U.S.-based venture capital firms. Acquired Accern (2025-02-07). - **Growth Signals**: - Headcount of 48 employees (+5.8% YoY), operating in 13 countries - 21 active job postings (monthly job postings up +200%) - LinkedIn followers: 113,174 (monthly +0.1%, yearly +1.6%) - Talent sourced from Amazon Web Services, Microsoft, IBM, Jasper, and others - SOC2-ready with flexible deployment (on-premise, private cloud, hosted) ## Competitive Advantages - **Full-Stack Workforce OS**: One platform for creation, execution, collaboration, and governance — unlike point solutions that only automate tasks. - **Governed Autonomy**: Agents stay budget-positive and compliant under built-in rules and guardrails, with decision tracking and accountability dashboards. - **Human-First Adoption**: Real-time chat, clear escalation paths, and role-based controls keep people in command. - **Self-Evolving Intelligence**: Agents retrain, build new agents, and refine workflows automatically, compounding ROI over time. - **Interoperability**: Agents work across systems, tools, and departments, eliminating silos and enabling unified operations. ## Strategic Focus - **Category creation**: Defining the “Agentic Labor Infrastructure” category and becoming the company the world thinks of first for using AI to create real business value. - **Scaling the hybrid workforce**: Enabling enterprises to move from single-process automation to full-division autonomy. - **Global expansion**: Operating in 13 countries with a growing presence in Middle East, Europe, and Asia. - **Talent acquisition**: Aggressively hiring across engineering, product, go-to-market, and marketing to build foundational infrastructure at the frontier of AI. ## Why Work Here - **Culture highlights**: “Real ownership from day one” and “the chance to help define a category, not iterate inside one.” The team includes talent from DeepMind, Google Brain, Microsoft Research, and other industry leaders. - **Remote/hybrid/office policy**: Not explicitly stated, but the company operates across 13 countries with employees in the US, India, UAE, Israel, Ukraine, Poland, Netherlands, Denmark, Switzerland, Germany, and more — suggesting a distributed/remote-friendly model. - **Notable perks or engineering culture**: - Employer rating: 3.7/5.0 (13 reviews) — Compensation rated 3.9, Career growth rated 3.9 - Work on foundational infrastructure at the frontier of AI (agent runtime, orchestration layer, customer-facing products) - SOC2-ready security posture and enterprise-grade deployment options - Opportunity to work on a product that “will change how organizations everywhere operate” - **Open roles include**: Head of Product Security & Compliance, Head of Engineering (Organization & Governance), Senior Backend Engineer, Senior ML Engineer, Senior Site Reliability Engineer, Customer Solutions Engineer, Engineering Manager (Full Stack/Frontend), and more. ## Sources 1. [wand.ai](https://wand.ai/) 2. [wand.ai/careers](https://wand.ai/careers) 3. [LinkedIn - Wand AI](https://www.linkedin.com/company/wand1) 4. [wand.ai - Meet Wand: What We Build, Why It Matters](https://wand.ai/blog-old2/meet-wand-what-we-build-why-it-matters) 5. 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