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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. 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. Be cautious of emails from other domains. Legitimate LaunchDarkly recruiters will never ask for money, fees, or banking information before making a job offer. LaunchDarkly will never make a job offer without conducting a formal interview process. Our interview process does not involve asking detailed questions by email. If you are ever unsure about a communication that you receive, don't click any links—visit Careers | LaunchDarkly directly for confirmed job openings and links to apply. Please notify us of any fraudulent representation by sending an email to careers@launchdarkly.com .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.