Staff Engineer, LaunchDarkly
Experimentation Team
$182.6-295.4k
Salary dependent on location
Feature management for software teams
Be an early applicant
Feature management for software teams
501-1000 employees
Be an early applicant
$182.6-295.4k
Salary dependent on location
501-1000 employees
Company mission
To empower all teams to deliver and control their software.
Role
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
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
- 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
- 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
Our take
Feature flags are a powerful software development tool, allowing teams to modify system behavior without changing code, therefore minimizing risk. If a business has a new feature they want to test, they can then roll it out to a small set of users before letting it go live. These companies can, for example, get quick feedback on the new feature and fix any bugs, or roll back any changes.
LaunchDarkly allows companies to run tests like this by activating the new code for only a small set of users prior to launch. Typically running these tests requires lots of developer time - a daunting and expensive prospect for most rapidly growing businesses - and can risk disruption to the user experience if testing takes place on the live product.
Demonstrating its value to businesses, the platform is used by the likes of IBM, Intuit and Atlassian. Recently it has seemed to focus on further scaling its growth as it develops towards being a leading business in the feature management space.
Freddie
Company Specialist at Welcome to the Jungle
Insights
0% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Aug 2021
$200m
SERIES D
Jan 2020
$54m
LATE VC
Total funding: $330.3m
Company 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
Company HQ
Lakeside, Oakland, CA
Leadership
Edith Harbaugh
(Co-founder, Executive Chair & CEO)Worked as a Product Manager at Vignette, Monster Worldwide, and PlantSense. Previously Product Director at TripIt.
John Kodumal
(Co-founder & CTO)Prior to co-founding LaunchDarkly was Software Development Manager at Atlassian and Bytecode Anaylsis Architect for Coverity.
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