Staff Engineer – Experimentation Team
LaunchDarkly - Remote - US
Posted Apr 16, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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Where they hire
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About this role
Staff Engineer – Experimentation Team Remote - US About the Job: 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 example: setting the bar for rigor, mentoring teammates, and owning systems end to end, including on-call. Responsibilities: - Build the experimentation statistical engine - hypothesis testing, sequential analysis, variance reduction ( CUPED , Winsorization), power analysis. Ensure statistical correctness across all experiment types. - Design warehouse-native experimentation that runs analysis inside customer warehouses (Snowflake, Databricks, Redshift, BigQuery). Build modular, warehouse-agnostic abstractions for rapid new backend support. - Lead adaptive experimentation - contextual bandit systems, Bayesian optimization, automated allocation beyond simple A/B
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