Role description
AxiamaticAxiamatic builds an agentic platform that de-risks enterprise transformation programs: the multi-year ERP, CRM, and supply-chain overhauls that routinely slip on cost and timeline. Our agents ingest a program’s real signals, catch risk before it compounds, and route remediation to the right people. Fortune 500 programs already run on it. (More at axiamatic.com.)
We’re a Series A startup backed by multiple top-tier Silicon Valley VCs, with founders who have built and exited companies together. Two problems sit at the center of the work: turning messy, real-world program data into structured knowledge, and building the agent layer on frontier LLMs that acts on it.
What you’ll do
• Help shape the architecture and engineering practices for our agent-orchestration layer on top of frontier LLMs.
• Turn heterogeneous program data (plans, status reports, risk registers, meeting notes) into structured, queryable knowledge that agents can reason over.
• Build services that stay reliable, performant, observable, and cost-efficient across conventional and LLM-driven workloads.
• Own backend features end to end, from problem definition and technical design through deployment and production operation.
• Work directly with product management, and with the messy realities of how customers actually operate, to decide what’s worth building, not just how to build it.
• Review code, write tests, and share responsibility for what’s running in production.
Requirements
• BS, MS, or PhD in Computer Science or a related field.
• 2-4 years building backend systems in production, ideally for AI-powered or data-intensive products.
• Strong Python and solid backend fundamentals: API design, concurrency, distributed services, testing, and production debugging. Some of our services are in Java, so you’re comfortable there or ready to pick it up.
• Experience shipping an LLM-powered feature to production, with real ownership of how it behaves, not just a prototype. Familiarity with model APIs, retrieval, and agent workflows.
• A feel for structuring messy, real-world data: you’ve taken unstructured or heterogeneous sources and turned them into something a system can rely on.
• Hands-on experience building on a public cloud, AWS preferred.
• Comfortable in a fast-moving environment where priorities evolve with the product; you’re the kind of engineer who takes an open-ended problem, shapes the approach, and drives it to something shipped.
• Startup experience is a plus.
Work model: Hybrid, 3 days a week in our Menlo Park office