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Profound

Software Engineer, Backend

New York, New York$140k–$230kfulltimemidAdded 1 month ago

About this role

Profound seeks a Backend Engineer to build scalable infrastructure for an AI-driven marketing platform used by Fortune 500 companies. You'll design high-performance systems, optimize data pipelines, and develop APIs that help businesses understand their presence in AI search results.

What you'll do

  • Architect and develop high-performance backend systems for AI-driven analytics
  • Build and maintain scalable APIs for real-time insights and data retrieval
  • Optimize data processing pipelines for large-scale structured and unstructured data
  • Ensure system security, performance, and reliability across the stack
  • Collaborate with front-end engineers and data scientists on feature delivery
  • Own key technical decisions balancing speed with maintainability

What they're looking for

  • Node.js, Python, or Rust
  • Scalable backend architecture
  • PostgreSQL, MySQL, or OLAP databases and query optimization
  • Data pipeline design and distributed systems
  • AWS, GCP, or Azure cloud infrastructure
  • Docker and Kubernetes containerization
  • API security, authentication, and performance tuning
  • Fast-paced startup environment adaptability

Benefits

  • Competitive base salary ($140,000–$260,000 for NYC/SF)
  • Equity compensation
  • Full range of benefits and perks
  • Visa sponsorship available
  • On-site roles in NYC, SF, or Buenos Aires
  • Opportunity to work on AI frontier at well-funded startup
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Profound

Profound builds an AI-native marketing platform used by Fortune 500 companies that automates lead qualification, scoring, and routing while capturing prospect intent signals. The company is hiring Infrastructure Engineers, Data Engineers, Design Engineers, GTM Engineers, and Talent Engineers to scale its cloud systems, data pipelines, user experiences, and internal recruiting automation.

View all jobs at Profound

Likely interview questions

  • Walk us through a backend system you've built that handles large-scale data. How did you approach scalability and what were the key bottlenecks?
  • Describe your experience optimizing database queries and data pipelines. What tools or techniques have you used to improve performance?
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