Full Stack Engineer, Growth

Stripe Stripe · Fintech · Singapore · 8916 Growth - Eng

Full Stack Engineer on the Growth team at Stripe, focusing on building and shipping experiments and product improvements across stripe.com and the Dashboard to help businesses find and get started on Stripe. This role involves implementing ML-driven recommendations, experimentation frameworks, and growth optimization features, working with data scientists and product managers to translate growth hypotheses into technical implementations and A/B tests. The engineer will also contribute to shared platforms and tools for rapid experimentation and debug production issues.

What you'd actually do

  1. Build and maintain scalable systems and customer experiences that help businesses discover, onboard, and grow with Stripe's products
  2. Develop high-quality, robust backend APIs in Ruby that power responsive frontend experiences using React/JavaScript/CSS
  3. Implement and iterate on ML-driven recommendations, experimentation frameworks, and growth optimization features
  4. Work with data scientists and product managers to translate growth hypotheses into technical implementations and A/B tests
  5. Contribute to shared platforms and tools that enable rapid experimentation across growth initiatives

Skills

Required

  • 2-5 years of industry software engineering experience
  • Strong coding skills in any programming language
  • Experience delivering small projects independently and contributing to medium projects with guidance
  • Strong collaboration skills
  • Ability to thrive on a high level of autonomy, responsibility, and think of yourself as entrepreneurial
  • Interest in working as a generalist across varying technologies and stacks
  • Experience building, maintaining, and running production services and backend APIs
  • Deep care for users' needs

Nice to have

  • Experience with machine learning
  • recommender systems
  • product-led growth
  • lifecycle marketing
  • Familiarity with basic analysis in large datasets (especially in systems like Redshift or Presto/Trino)
  • Experience incorporating observability into production systems and supporting their operation (oncall, incident response)

What the JD emphasized

  • ML-driven recommendations
  • experimentation frameworks
  • growth optimization features
  • A/B tests

Other signals

  • ML-powered recommendations
  • growth experimentation engines
  • A/B tests