engineering
Posted Apr 16Staff Engineer – Experimentation Team
at LaunchDarkly
United StatesRemote
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.
- Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar. •
Requirements
- 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.
- Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL -based allocation. •
- 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. •
Experience
- 10+ years building large-scale experimentation platforms, statistical analysis systems, or data-intensive backend services. •
Benefits
- 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. About LaunchDarkly:
Contact
- To do so, contact People Ops at hr@launchdarkly.com .
- Your safety matters to us. To protect yourself from potential scams, LaunchDarkly recruiters will only contact you from @ launchdarkly.com email addresses or via LinkedIn from "Verified Recruiter" accounts.
- Please notify us of any fraudulent representation by sending an email to careers@launchdarkly.com .
Additional details
- 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 lead by example: setting the bar for rigor, mentoring teammates, and owning systems end to end, including on-call. Responsibilities: •
- Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction ( CUPED ), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs. •
- Technical leadership: setting direction, breaking down complex problems, influencing across teams. •
- Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise.
- Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software.
- The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong.