Ai, Search & Knowledge - Senior Software Development Engineer, Tech Ops

Apple Apple · Big Tech · Seattle, WA · Machine Learning and AI

This role focuses on the reliability engineering of ML compute platform infrastructure, ensuring scalability, stability, and efficient resource utilization. The engineer will manage the entire lifecycle of ML compute platform reliability, from addressing user queries to proactively improving system performance and implementing operational tools and processes. The role requires strong software development skills in languages like Python and Golang, experience with large-scale ML services, and familiarity with orchestration frameworks like Kubernetes.

What you'd actually do

  1. Ensure user queries or tickets to be responded in time
  2. Evaluate current visibility for state and performance of the system
  3. Define and monitor system key performance indices
  4. Design and implement operational tools and protocols, along with CI/CD processes
  5. As a technical leader, motivate and communicate cross-teams, drive the understanding of problems and the best practice of solutions

Skills

Required

  • Ability of analyzing problems in depth, determining root cause, articulate clearly and propose solutions
  • Solid understanding of system architecture and large-scale ML service and computational platform operations
  • Ability of driving a project, starting from problem statement, requirement and criteria definition, solution design, implementation, deployment until post-deployment operations; achieving the goal through a teamwork or even cross-team collaborations
  • Proficiency in coding with scripting and programming languages, including but not limited to - Bash, Python, Golang
  • 7+ years experience of software development for compute infra or its operational stack, commensurate with operating cutting-edge hybrid cloud platforms

Nice to have

  • Knowledge of ML, including LLM, as well as experience in developing real, large scale ML jobs
  • Knowledge of ML training and production workflows, understanding dependencies among architectural building blocks
  • Knowledge of analytics method and pipelines, able to utilize it for visualization of platform KPIs
  • Experience designing and implementing systems to support ML applications
  • Experience in large-scale service and job deployment, using an orchestration framework (Kubernetes) and cloud services for large-scale projects
  • Experience in observability of system behaviors, having made decision what should be visible according to actual needs to solve specific problem
  • Experience and knowledge on Quality Assurance, A/B testing for large-scale systems

What the JD emphasized

  • large-scale ML service and computational platform operations
  • large-scale service and job deployment
  • operating cutting-edge hybrid cloud platforms

Other signals

  • ML compute platform reliability engineering
  • large-scale ML service and computational platform operations
  • large-scale service and job deployment