Asks for

The ad says“Cloud environments (AWS, GCP) with infrastructure-as-code.”
The ad says“Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL-based allocation.”
The ad says“Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar.”
The ad says“Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL-based allocation.”
The ad says“Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.”
The ad says“Experience with event-driven architectures, data pipelines, and large-scale data processing.”
The ad says“Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar.”
The ad says“Experience with event-driven architectures, data pipelines, and large-scale data processing.”
The ad says“Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.”
The ad says“Cloud environments (AWS, GCP) with infrastructure-as-code.”
The ad says“Expertise in Go, Python, or similar for backend services and statistical computation.”
The ad says“Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.”
The ad says“Cloud environments (AWS, GCP) with infrastructure-as-code.”
The ad says“Experience with event-driven architectures, data pipelines, and large-scale data processing.”
The ad says“Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.”
The ad says“Expertise in Go, Python, or similar for backend services and statistical computation.”
The ad says“Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar.”
The ad says“Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL-based allocation.”
The ad says“Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.”
The ad says“Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar.”
The ad says“Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL-based allocation.”
The ad says“Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.”

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.

About LaunchDarkly:

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. Developers can target product experiences to any customer segment and maximize the business impact of every feature. And by gradually rolling out new application components, they escape nightmare "big-bang" technology migrations.

The LaunchDarkly platform was built to guide engineers to the next frontier of DevOps by:

Improving the velocity and stability of software releases, without the fear of end customer outages
Delivering targeted experiences by easily personalizing features to customer cohorts
Maximizing the business impact of every feature through the ability to experiment and optimize
Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types
Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability

At LaunchDarkly, we believe in the power of teams. We're building a team that is humble, open, collaborative, respectful and kind. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status. LD invites any applicant to review our written Affirmative Action Plan. To do so, contact People Ops at hr@launchdarkly.com.

Do you need a disability accommodation?

Fill out this accommodations request form and someone from our People Operations team will contact you for assistance.

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