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
Reason clearly about code, architecture, and whole-system behavior even when AI is in the loop
You are kind, coachable, and excited to learn from senior engineers
You already use AI regularly in your engineering work and can explain how, when, and why
You enjoy understanding how real systems behave in production, not just how they are supposed to work on paper
You are curious about scale, failure, caching, delivery, and operability
You are energized by debugging, tradeoffs, and steadily improving systems over time
You bring real backend or infrastructure ownership, not just feature implementation
You care about writing software that is maintainable, observable, and dependable
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; today the team primarily uses Go, with some Rust in the broader environment
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
Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $171,200 - $235,400
Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $154,100 - $211,860
Zone 3: All other US locations - $145,500 - $200,090
Improving the velocity and stability of software releases, without the fear of end customer outages
Delivering targeted experiences by easily personalizing features to customer cohorts
Maximizing the business impact of every feature through the ability to experiment and optimize
Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types
Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability