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David AI

Full Stack Engineer

San Francisco$165k–$225kfulltimemidAdded 1 month ago

About this role

David AI, a rapidly growing audio data research company founded by former Scale AI engineers, is seeking a Full Stack Engineer to build user-facing tools and scalable data processing systems for audio AI applications. You'll work across the entire stack—frontend to backend to ML—shipping features that help thousands of users derive insights from terabytes of speech data.

What you'll do

  • Develop and ship full-stack features used by thousands of users daily
  • Build scalable data processing pipelines that extract insights from large audio datasets
  • Implement LLM and digital signal processing solutions to enhance customer understanding of datasets
  • Collaborate closely with researchers and operations teams to iterate on data collection interfaces
  • Stay current with emerging technologies in software engineering, ML, and signal processing
  • Move fast and own work end-to-end, deploying to production regularly

What they're looking for

  • Full-stack web development
  • TypeScript and modern JavaScript frameworks
  • Backend systems design and scalability
  • Database design (PostgreSQL)
  • Cloud infrastructure (AWS)
  • Rapid prototyping and iteration
  • Production-grade software craftsmanship
  • Machine learning and audio experience (nice-to-have)

Benefits

  • Unlimited PTO
  • Comprehensive health, dental, and vision coverage with 100% coverage for most plans
  • FSA and HSA access
  • 401(k) access
  • Meals provided twice daily via DoorDash plus office snacks
  • Unlimited company-sponsored Barry's fitness classes
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David AI

David AI builds audio data research platforms that help customers process and understand massive volumes of audio data for AI model training. The company is hiring software engineers across backend, full-stack, security, and product engineering roles to develop scalable infrastructure, distributed systems, and user-facing tools.

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Likely interview questions

  • Walk us through a full-stack feature you shipped that scaled to many users. How did you approach the frontend, backend, and deployment?
  • Describe your experience building data processing pipelines. How have you handled scaling challenges when working with large datasets?
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