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 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.
Qualifications:- 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.
Pay:Target pay ranges based on Geographic Zones* for Level 5:
- Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $214,800 - $295,350*
- Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $193,400 - $265,870**
- Zone 3: All other US locations - $182,600 - $251,0202**
LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location.
*Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas.
**Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary.
