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Grid

Product Engineer (Tax & Banking) | San Francisco

San Francisco, California$120k–$200kfull timemidAdded 1 month ago

About this role

Grid seeks a Product Engineer to scale and monetize PayBoost, an AI-powered tax optimization product serving millions of Americans. You'll collaborate with business and engineering teams to enhance the tax service's architecture, reliability, and monitoring while handling mission-critical financial systems.

What you'll do

  • Design and develop scalable tax product features alongside business operations teams
  • Upgrade AI architecture and potentially migrate the service from the monolith
  • Maintain 99.99% reliability and uptime for tax optimization systems
  • Enhance data models and create monitoring visibility for core components
  • Operate and optimize mission-critical financial systems end-to-end
  • Collaborate with core services engineering on system performance and scale

What they're looking for

  • Backend development (Go preferred)
  • GCP and cloud infrastructure
  • MySQL and BigQuery databases
  • Protocol buffers (protobufs)
  • Data modeling and warehousing
  • System design and architecture
  • Monitoring and observability
  • Financial systems or tax domain knowledge

Benefits

  • In-person work in San Francisco or Seattle offices
  • High autonomy and ownership of mission-critical systems
  • Opportunity to impact financial inclusion for millions
  • Collaborative team culture focused on learning and growth
  • Work on cutting-edge financial technology and AI systems
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Grid

Grid builds PayBoost, an AI-powered tax optimization product serving millions of Americans. The company is hiring engineers and product engineers to scale its platform, improve system architecture, and enhance reliability for mission-critical financial services.

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Likely interview questions

  • Walk us through your experience building or scaling financial products. How did you approach reliability and uptime requirements?
  • Describe a time you've worked with AI or machine learning systems in production. What challenges did you encounter?
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