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# Senior Software Engineer, Backend - Platform...

- **Company:** Coinbase, Inc.
- **Location:** Baton Rouge, LA, United States
- **Experience:** Expert
- **Salary:** $186,065.0 - $218,900.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Distributed Systems, Amazon DynamoDB, PostgreSQL, MongoDB, Blockchain, Ruby, Software Engineering, Data Streaming, Apache Spark, Backend, Apache Flink, Ethereum, Apache Kafka, Docker, Golang
- **Published:** July 31, 2026
- **Apply:** https://www.juju.com/job/00000000gkowlk

## About the Role

* 5+ years of software development experience with large-scale distributed systems.

 * Strong sense of correctness - not just "does the code work" but "is the system as a whole behaving correctly."

 * Experience building platform-level infrastructure used by multiple teams.

 * Strong ownership: you hold the problem, not just the ticket.

 * Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality.

*Nice to haves*:

 * You have gone through a rapid growth in your company (from startup to mid-size).

 * Experience with financial, payments, or reconciliation systems.

 * Streaming data infrastructure (Kafka, Flink, Spark, or similar).

 * You've worked with Golang, Ruby, Docker, Postgres, MongoDB or DynamoDB.

 * You've built financial, high reliability or security systems.

 * Crypto-forward experience, including familiarity with onchain activity such as interacting with Ethereum addresses, using ENS, and engaging with dApps or blockchain-based services.

## Description

* Contribute to the core platform: the detection engine, invariant framework, and tooling that partner teams use to onboard their fund flows.

 * Own workstreams end-to-end: from designing correctness models with partner teams to operating them in production.

 * Build AI guardrails that close the velocity gap: predictive consistency models that flag deviations at initiation (not after settlement), and proactive invariant discovery that surfaces coverage gaps before any team asks - before incidents, not because of them.

 * Mentor junior engineers and lead technical execution on high-impact projects.


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