Staff Software Engineer (applied Ai)

Apple Apple · Big Tech · Cupertino, CA · Machine Learning and AI

Staff Software Engineer to advance Manufacturing Systems and Operations using LLMs, from prototyping to production and scaling. This role involves working with various organizations to develop impactful solutions, some standalone and others integrated into existing systems.

What you'd actually do

  1. Move fast prototyping ideas and experimenting with new approaches to solve existing problems better.
  2. Develop solutions that not only end up in production, but have a significant impact.
  3. Take an idea from the whiteboard to production and then scale it.
  4. Build robust HTTP APIs and backend services.
  5. Leverage APIs from the latest LLMs.

Skills

Required

  • 10+ years in a senior role working across the entire tech stack with a skilled team.
  • 10+ years building robust HTTP APIs and backend services
  • Expert level grasp of at least 1 modern programming language: Go (preferred), Python, Java, etc.
  • Experience building solutions that leverage API’s from the latest LLMs.

Nice to have

  • Experience with model pipeline and registry tools, detecting and preventing model drift, automating model monitoring, and ensuring model accuracy
  • Experience building robust evaluations for prompt optimization and tuning ML workflows
  • Experience with RAG and modern model in context learning techniques
  • Experience with SQL and database systems such as PostgreSQL
  • Experience with building ETL pipeline in data warehouse such as Snowflake
  • Manufacturing experience or exposure is a plus, but not required.

What the JD emphasized

  • 10+ years in a senior role working across the entire tech stack with a skilled team.
  • 10+ years building robust HTTP APIs and backend services
  • Expert level grasp of at least 1 modern programming language: Go (preferred), Python, Java, etc.

Other signals

  • Develop solutions that not only end up in production, but have a significant impact.
  • Experience taking an idea from the whiteboard to production and then scale it.
  • Leverage APIs from the latest LLMs.