Full-Time
Staff Machine Learning Engineer
Agent Memory & Reasoning, University
Wand Synthesis AI Inc
51-200 employees
No-code self-service AI platform for enterprises
No salary listed
Remote in USA
Remote
Master's, PhD
 AI & Machine Learning (1) |
|---|
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- 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.
- 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.
- 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
Wand.ai is a self-service AI platform for enterprises that lets business users access AI-powered data analytics without coding. It works by using natural language to interact with data and build AI-driven solutions through a user-friendly interface, removing the need for complex setup. The platform likely offers subscription tiers with add-on services like premium support and custom AI model development, providing a recurring revenue model. Wand.ai differentiates itself from competitors by focusing on no-code, natural-language interaction for non-technical business users and scalable, enterprise-grade analytics. The company’s goal is to democratize AI in large organizations, helping them boost productivity and data-driven decision-making across finance, healthcare, retail, and manufacturing.
Company Size
51-200
Company Stage
Series C
Total Funding
$22M
Headquarters
Tel Aviv-Yafo, Israel
Founded
2022
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Simplify's Take
What believers are saying
- July 21 2026 Protopia AI strengthens shared sovereign compute without plaintext exposure.
- August 12 2026 VLNO hardens open-weight models against adversarial attacks for regulated buyers.
- August 18 2026 StorONE cuts flash storage needs for sovereign AI deployments.
What critics are saying
- Wand’s sovereign pitch targets governments and regulated enterprises, concentrating sales cycles around procurement.
- Microsoft Copilot, AWS Bedrock, and Google Gemini can bundle similar agents by 2027.
- A security breach in shared sovereign infrastructure would destroy trust and stall deployments.
What makes Wand Synthesis AI Inc unique
- Wand’s 2025 Accern acquisition adds real-time financial and compliance data to agent workflows.
- Its sovereign stack bundles governance, auditability, privacy, and robustness in one platform.
- Wand positions AI agents as managed workforce infrastructure, not standalone copilots.
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Benefits
Hybrid Work Options
Growth & Insights and Company News
6 month growth
↓ -1%1 year growth
↓ -2%2 year growth
↓ -1%Wand AI has integrated StorONE's Real-Time Tiering technology into its sovereign AI infrastructure offering. The partnership aims to improve storage efficiency for national AI programmes by reducing flash storage requirements whilst maintaining performance. StorONE's technology writes data to flash storage initially, then continuously moves inactive blocks to high-capacity media within the same volume. This eliminates the need for restore steps whilst keeping all data immediately accessible. The company reports approximately 90% average flash savings at roughly 10% cost-performance impact. One enterprise customer reduced their footprint from nine cabinets to two, achieving an 80% smaller footprint and 78% reduction in power consumption. The storage efficiency layer complements Wand's existing sovereign AI capabilities, which include inference privacy and confidential computing. The joint reference architecture is now available for sovereign programmes and enterprises.
Wand AI has integrated VLNO's model robustness technology into its sovereign AI platform, enabling governments and regulated enterprises to deploy open-weight models on their own infrastructure with certified security. VLNO continuously tests customer models against adversarial attacks and generates training data to strengthen them, with protections incorporated directly into the model weights. The integration addresses a key barrier to production deployment of open-weight models, which often lack standardised safety evaluations and proven adversarial robustness. Before models enter Wand's Open Model Registry, VLNO benchmarks them against adaptive attacks. Models failing to meet robustness thresholds receive adversarial training data that customers can incorporate into their fine-tuning pipelines. The joint reference architecture is now available for sovereign programmes and regulated enterprises. Wand AI, founded in 2023 and headquartered in Palo Alto, provides sovereign AI infrastructure for deploying AI agents.
Wand AI and Protopia AI have partnered to deliver sovereign AI infrastructure that allows governments to run sensitive workloads on shared computing resources without exposing data. The collaboration combines Wand's operating system for hybrid human-AI workforces with Protopia AI's Stained Glass Transform privacy technology. The solution addresses a critical national security challenge: currently, different government ministries and agencies must maintain separate infrastructure for sensitive data, resulting in massive underutilisation of computing resources. Protopia's technology converts sensitive inputs into protected representations at the trust boundary, allowing multiple agencies to share one sovereign backend whilst keeping their data secure. The joint architecture aims to increase national compute utilisation from roughly 25% to over 80%, reducing costs for participating organisations. The solution is available now to sovereign programmes and enterprises. Founded in 2023, Wand AI is headquartered in Palo Alto. Protopia AI is based in Austin, Texas.
Wand AI and Protopia AI partner to power sovereign AI on shared, secure, high-utilization backend. Wand AI's agentic workforce operating system paired with Protopia AI's Stained Glass inference privacy layer. Every ministry and agency in a nation can run sensitive workloads on a single sovereign AI back end, with plaintext never leaving the trust boundary. Wand AI and Protopia AI today announced a partnership to deliver sovereign AI at national scale. The collaboration combines Wand's operating system for the hybrid human and AI workforce with Protopia AI's inference privacy layer, enabling a government or enterprise to run its most sensitive workloads across one shared sovereign backend at full utilization, without exposing data to the infrastructure operator or to other tenants. Real sovereignty requires the entire AI stack to run on sovereign soil and sensitive data. The agentic layer can be sovereign, but the moment an agent sends that sensitive data to a model, that data is exposed in plaintext across the entire serving environment: logs, caches, GPU memory, traces, and runtime infrastructure. That creates a hard national-security problem. Secrets from different ministries, agencies, banks, and critical infrastructure operators cannot safely share the same backend, so the default today is fragmentation, a separate infrastructure stack for each data owner. The result is massive waste: national compute investments sit underutilized because every sensitive workload requires its own isolated environment. At country scale, this "isolation tax" can reach tens of billions in infrastructure spend over time. The alternative is worse: data that is sovereign in name, but not sovereign in fact. The Wand and Protopia AI partnership removes that tradeoff. Wand provides the sovereign agentic stack: country-level governance, a hybrid human and AI operating system, certified AI labor, and autonomous agents, attached to the sovereign compute base. Protopia AI provides the inference privacy layer. Its Stained Glass Transform(TM)(SGT), applied by the SafeCLAW subagent at the trust boundary, converts sensitive inputs into protected representations, so only the protected form ever crosses into the model and compute layers, with no decoder on the host and accuracy parity to plaintext. Because plaintext never leaves the trust boundary, every data owner can share one sovereign backend without exposing data to the operator or to one another. Too often, sovereign AI programs have had to fragment their agentic infrastructure into dedicated stacks whenever sensitive data enters the workflow. Wand and Protopia AI remove that constraint. Protopia Stained Glass keeps plaintext inside each data owner's trust boundary, so ministries and agencies can run their highest-value agentic workloads on one governed sovereign backend and make full use of the compute already in place.
Wand AI acquires Accern to boost real-time data capabilities, empowering AI agents to deliver faster insights and autonomous enterprise workflows.