Staff Engineer – Experimentation Team

LaunchDarkly

About The Position

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.

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.

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 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.