Founding Applied AI Engineer

About the role

ABOUT DOSS

DOSS IS BUILDING AN OPERATIONS CLOUD FOR THE REAL WORLD.

A modern, AI-native platform for physical product businesses to manage the flow of goods, dollars, and data in real time, across:

- Procurement

- Inventory

- Orders

- Fulfillment

- Finance

Built to replace spreadsheet chaos and rigid, consultant-heavy ERPs. Fast time-to-value, adaptable as the business evolves.

Through our Adaptive Resource Platform (ARP) and unified operational data model, teams deploy quickly, automate workflows, and make changes without months of re-implementation.

We recently raised a $55M Series B https://www.doss.com/news/doss-raises-55m-series-b, co-led by Madrona and Premji Invest. Participation from Intuit Ventures, Theory Ventures, General Catalyst, Contrary Capital, and Pathlight VC.

DOSS is trusted by fast-growing operators to run critical operations with speed, control, and confidence.

About the role:

Every ERP claims to be "AI-powered." Almost none of them let an operator ask a question in plain language and get an answer grounded in their live procurement, inventory, and finance data, let alone let an agent safely take action on it. That's the goal of this job: you'll own applied AI across Doss: Dossbot (query, analyze, and automate via chat), AI-assisted schema and workflow generation, and the agentic automations that turn back-office busywork into background processes. You'll be the technical anchor for how foundation models meet a transactional system of record.

What you’ll do:

- Own the applied AI architecture end-to-end: model selection, orchestration, RAG over the unified operational data model, evals, guardrails, and cost/latency budgets in production.

- Build Dossbot into the primary interface for operations: natural-language search, analysis, and automation grounded in live supply-chain, finance, and ops data.

- Engineer AI-assisted tooling that uses foundation models to propose, validate, and apply customer schemas and workflow definitions on live production tenants.

- Design agentic workflows that act inside enterprise guardrails: permission-aware, auditable, tenant-isolated, and safe to run against a customer's system of record.

- Stand up the eval and observability infrastructure to measure quality, catch regressions, and proactively detect anomalous or risky emergent behavior.

- Partner with product, design, and our Forward Deployed team to deliver on their highest-leverage AI use cases.

- Mentor and shape engineering culture as the team's center of gravity for applied AI, raising the bar on how we build, evaluate, and ship LLM-powered systems.

What we’re looking for:

Experience

- Shipped LLM-powered features or agents to production and operated them at scale, not just prototypes or demos.