Staff Engineer

Who you are

• 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
• Deep technical experience, a scientific mindset, and the ability to influence product and technical direction are critical
• We want someone who brings genuine depth in applied statistics and ML — as fluent in statistical validity as in system architecture

What the job involves

• 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
• 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
• You'll lead by example: setting the bar for rigor, mentoring teammates, and owning systems end to end, including on-call
• 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

Benefits

• Full Vision
• Full Dental
• Full Medical
• Flexible Schedule
• Healthy Lunches & Snacks
• Conference & Meetup Access
• Education Stipend
• Personalized Work Station
• Close to Public Transportation
• Bike Parking
• Team Activities
• Unlimited paid time off policy
• Work from home opportunities
• Health insurance