LaunchDarkly helps teams safely manage code and AI agents in production with feature flags, progressive delivery, automated rollback, and runtime control. Backed by Bessemer, Insight and 500 Global.
About the role
As a Staff Engineer on LaunchDarkly's Experimentation team, you'll build the platform that helps engineering teams make data-driven decisions with confidence. Our Experimentation product enables customers to run A/B tests, measure the impact of feature changes, and optimize experiences — integrated with a feature management platform that processes trillions of evaluations daily.
What they're looking for
- 10+ years building large-scale experimentation platforms, statistical analysis systems, or data-intensive backend services
- Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction ( CUPED ), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs
- Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL -based allocation
- Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar
- Expertise in Go, Python, or similar for backend services and statistical computation
- Experience with event-driven architectures, data pipelines, and large-scale data processing
More about this role
As a Staff Engineer on LaunchDarkly's Experimentation team, you'll build the platform that helps engineering teams make data-driven decisions with confidence. Our Experimentation product enables customers to run A/B tests, measure the impact of feature changes, and optimize experiences — integrated with a feature management platform that processes trillions of evaluations daily.
This role sits at the intersection of data science and platform engineering. You'll design the statistical engine, warehouse-native analysis pipelines, and adaptive experimentation systems (including contextual bandits) that power our customers' most important decisions. We want someone who brings genuine depth in applied statistics and ML — as fluent in statistical validity as in system architecture.
You'll also architect warehouse-agnostic features that run analysis directly inside customers' data warehouses (Snowflake, Databricks, Redshift, BigQuery) — modular computation layers that abstract across warehouse environments while maintaining statistical correctness.
Deep technical experience, a scientific mindset, and the ability to influence product and technical direction are critical. You'll lead by...
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