Machine Learning Engineer, Ois-core Engine

Amazon Amazon · Big Tech · Austin, TX · Software Development

Machine Learning Engineer role focused on designing, implementing, testing, deploying, and maintaining ML solutions to improve network management and infrastructure operations for Amazon's customers. The role involves solving complex technical problems at scale, partnering across diverse teams, and contributing to intelligent, efficient infrastructure.

What you'd actually do

  1. Solve complex technical problems, often ones not solved before, at every layer of the stack.
  2. Design, implement, test, deploy and maintain innovative ML solutions to transform service performance, durability, cost, and security.
  3. Build high-impact ML solutions to deliver to our large customer base.
  4. Build high-quality, highly available, always-on products.
  5. Research implementations that deliver the best possible experiences for customers.

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • 1+ years of Object Oriented Design experience
  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent

What the JD emphasized

  • complex network management problems
  • transform how businesses operate at scale
  • AI/ML team
  • intelligent, efficient infrastructure solutions
  • large customer base
  • complex business problems
  • generative AI and machine learning
  • complex technical challenges
  • innovative ML solutions
  • service performance, durability, cost, and security
  • high-impact ML solutions
  • large customer base
  • high-quality, highly available, always-on products
  • best possible experiences for customers
  • internal and external stakeholders
  • drive business solutions
  • startup-like development environment
  • complex network management challenges
  • optimize network performance
  • emerging techniques

Other signals

  • ML solutions for network management
  • improve service performance, durability, cost, and security
  • transform how businesses operate at scale