Backend Engineer, Flag Delivery
New
L
LaunchDarklySoftware Development
Remote - USFull-TimeSenior
Salary$145,500 - $235,400 (Base salary depends on Geographic Zones)
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Job Details
- Experience
- 5+ years
- Required Skills
- JavaGoRustDebuggingDistributed Systems
Requirements
- 5+ years of backend software engineering experience.
- Experience building or operating production backend systems.
- Strong programming skills in Go, Java, Rust, or a similar backend language.
- Clear, existing AI usage in engineering work.
- Ability to use AI without surrendering technical judgment.
- Familiarity with distributed systems fundamentals such as caching, concurrency, failure modes, and reliability.
- Experience debugging production issues and using observability to understand system behavior.
- Ability to reason clearly about code, architecture, and system behavior.
- Strong learning orientation and comfort working with senior engineers.
- Strong collaboration and communication skills, with the ability to work well across engineering teams.
- Willingness to participate in on-call and production support.
Responsibilities
- Build, improve, and operate backend services that power LaunchDarkly’s Flag Delivery systems.
- Contribute to systems that support streaming and polling delivery models for LaunchDarkly SDKs.
- Debug production issues, improve service reliability, and help the team maintain strong standards around latency, availability, observability, and operability.
- Work with technologies and patterns common to modern distributed backend systems, including caching layers, network services, concurrency, failure handling, and cloud infrastructure.
- Collaborate closely with partner teams including SDK, Platform, SRE, Security, and Enterprise to deliver improvements across the broader platform.
- Participate in the on-call rotation and take ownership of the systems you help build.
- Learn the existing architecture deeply and make pragmatic improvements that increase system clarity, performance, and maintainability.
- Use AI as part of your engineering workflow with sound judgment, including knowing when to use it, how to direct it effectively, and how to verify or reject its output.
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