Software Development Engineer, Alexa Audio

Amazon Amazon · Big Tech · IN, TN +1 · Software Development

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reduce latency, and influence the operational roadmap for core audio services. The position also requires mentoring junior engineers and contributing to hiring efforts.

What you'd actually do

  1. Design, implement, and deliver software in ambiguous and complex problem spaces of Alexa Audio working with partners such as product managers, technical program managers, and senior/principal engineers to deliver on the business goals related to Audio Experience.
  2. Create infrastructure to bring the power of LLMs into the Audio space while championing best practices for software engineering and operational excellence.
  3. Create tools and software to evaluate, analyze, and improve the model accuracy for various Audio experiences, while reducing latency and customer friction.
  4. Influence the operational and engineering excellence roadmap across core audio services set by the lead SDEs to proactively address peak readiness, hardware efficiency, scaling, throttling and improve availability & resiliency for Alexa Audio Services.
  5. Provide valuable design feedback around latency considerations, two-way door decisions and valuable coding feedback around coding standards, quality, versioning to both internal and external away teams, guide them on best practices for developing within Audio owned services and push back on proposals which do not adhere to our high quality standards.

Skills

Required

  • software development
  • design patterns
  • reliability and scaling
  • programming languages

Nice to have

  • full software development life cycle
  • coding standards
  • code reviews
  • source control management
  • build processes

What the JD emphasized

  • complex problem spaces
  • ambiguous
  • Generative AI
  • LLMs
  • model accuracy
  • reducing latency
  • operational excellence
  • customer friction
  • availability & resiliency
  • high quality standards

Other signals

  • integrating LLMs into audio experiences
  • improving model accuracy for audio
  • reducing latency for audio models