Doss

Staff Backend Engineer

San Francisco, California, United StatesFull timeStaff$230,000 - $260,000 / yearPosted 12 days ago
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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.

WHAT YOU'LL DO

- Design the core EAV data model & schema‑as‑code platform that powers every customer’s instance, balancing extreme flexibility with transactional integrity.

- Build a Git‑style version‑control + migration engine capable of rolling forward/back complex schema and data changes on live production tenants in enterprise settings.

- Engineer AI‑assisted tooling that uses foundation models to propose, validate, and apply new customer schemas and workflow definitions.

- Scale our white‑labeled data warehouse (BigQuery/Snowflake‐like) for high‑volume, low‑latency analytics and automation workloads.

- Create Terraform‑like declarative APIs & SDKs so ops teams can treat business processes as code while you ensure security, tenancy, and performance.

- Deliver end‑to‑end observability & search across supply‑chain, finance, and ops data—surfacing business logic in plain language through AI‐native search.

- Build Enterprise guard rails into a composable system to proactively detect anomalous behavior or risky emergent patterns

- Mentor and shape e...