Staff Engineer – Experimentation Team

🕒 April 16

🇺🇸 United States – Remote

💵 $182.6k - $295.4k / year

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

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LaunchDarkly

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

☁️ SaaS

🏢 Enterprise

💰 $200M Series D on 2021-08

SaaS • Enterprise

LaunchDarkly is a software company offering a platform for feature management and experimentation. Its solutions allow developers to control software releases through feature flags, enabling safeguarded and progressive rollouts. The platform integrates with various developer tools and supports multiple programming languages to streamline deployment and improve the developer experience. LaunchDarkly's solutions cater to industries including financial services, healthcare, high tech, retail, and government, among others, providing a robust infrastructure that aids in delivering customized and targeted user experiences.

📋 Description

• 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 tests. • Drive the platform roadmap with product, design, and data science. Shape what we build, not just how. • Collaborate cross-functionally with Warehouse Integrations, SDK, Platform, and Data Science teams. • Mentor engineers and raise the team's bar for statistical rigor and system design. • Own operational excellence — monitoring, observability, incident response, on-call. Robust telemetry and alerting.

🎯 Requirements

• 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. • Cloud environments (AWS, GCP) with infrastructure-as-code. • Technical leadership: setting direction, breaking down complex problems, influencing across teams. • Ability to translate statistical concepts for product and engineering audiences.

🏖️ Benefits

• Restricted Stock Units (RSUs) • Health, vision, and dental insurance • Mental health benefits