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# Software Engineer

- **Company:** Robinhood
- **Location:** Menlo Park, CA, United States
- **Experience:** Experienced
- **Salary:** $166,000.0 - $195,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Mobile Application Development, Software Engineering, Google Cloud, Enterprise Software Applications, Large Language Models, Backend, Serverless Computing
- **Published:** August 22, 2026
- **Apply:** https://www.dice.com/job-detail/015dee56-f58f-4681-9928-cabd31f7f118

## About the Role

* 3+ years of software engineering experience building and running production backend or full-stack systems, with a track record of independently delivering high-ambiguity projects end-to-end.
 * Hands-on cloud depth, Google Cloud Platform preferred, including containerized services, CI/CD pipelines, and infrastructure as code, with a strong focus on a serverless stack.
 * Demonstrated AI-native development: daily fluency with agentic coding tools, experience building LLM-powered applications or agentic workflows, and the judgment to review, harden, and productionize AI-generated code.
 * Product sense and scoping discipline: you can find the 20% of a prototype worth keeping, say no to the rest, and finish, this team's leverage depends on engagements that end.
 * Exceptional communication with non-engineers: you're comfortable embedded with finance, legal, or HR partners, translating in both directions without losing precision.
 * Experience in highly regulated industries, particularly finance or fintech, is strongly preferred.

## Description

* Assess vibe-coded prototypes from across the company and make the technical call: harden, rebuild, or redirect, then own the outcome end-to-end.
 * Design, build, and ship services on Robinhood's enterprise platforms, meeting the company's full standards for secure, reliable enterprise software.
 * Build AI-natively: drive agents through first-pass assessment, porting, and test scaffolding, and ship MCP servers, sub-agents, and agent skills as production artifacts where the solution calls for them.
 * Work directly with sponsoring business units (Finance, HR, Legal, Compliance) during each build, translating real workflows into software people actually adopt.
 * Deliver clean operational handoffs as part of your definition of done: runbooks, evals, and dashboards.
 * Establish the paved patterns with the Enterprise Platforms team: when you solve the same problem twice, make it a platform capability the third time.
 * Raise the bar on AI-era engineering quality, including rigorous, teaching-quality review of AI-generated code, your own and others'.


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