Staff Applied AI Engineer
Doss · San Francisco
About this role
**Staff Applied AI Engineer** **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 DOSS replaces spreadsheet chaos and rigid, consultant-heavy ERPs. With our **Adaptive Resource Platform (ARP)** and a unified operational data model, teams deploy quickly, automate workflows, and make changes without months of re-implementation. We recently raised a **$55M Series B** (co-led by Madrona and Premji Invest). Learn more: https://www.doss.com/news/doss-raises-55m-series-b **About the role** Every ERP claims to be “AI-powered.” Almost none let an operator ask a question in plain language and get an answer grounded in live procurement, inventory, and finance data—let alone let an agent safely take action. In this role, you’ll own applied AI across Doss: - **Dossbot** (query, analyze, and automate via chat) - **AI-assisted schema and workflow generation** - **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** as 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 **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 the 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/demos). - Deep hands-on work with foundation model APIs, **RAG pipelines**, tool use/function calling, prompt/context engineering, and fine-tuning where it earns its keep. - Built **eval harnesses** and **guardrails** for non-deterministic systems running against business-critical data. - Strong backend fundamentals: designing systems, **Postgres**, and cloud services for reliable, multi-tenant web applications. - Ability to go from **0 → 1** in ambiguous settings, with judgment for when AI is the right tool (and when it isn’t). **Qualities** - **Team player**: positivity, openness, and curiosity. - **Growth mindset**: learn and improve yourself, the team, and the company. - **Craftsmanship**: care about building something of quality. - **Customer First**: care deeply about the customer experience and creating a product full of delight. - **Pragmatic**: make the right decision based on circumstance, not theory. - **Low ego**: collabor
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