Distributed Systems Engineer, Instructional Products

Apple Apple · Big Tech · Seattle, WA +2 · Software and Services

This role involves developing backend services and creating data and inference pipelines with large datasets, focusing on enhancing search features using machine learning. It requires expertise in distributed systems, API design, and cloud services, with preferred experience in ML, NLP, and LLM tooling.

What you'd actually do

  1. developing backend services and creating data and inference pipelines with large set of data
  2. enhancing search features using advanced machine learning methods
  3. Experience of prompt engineering, fine-tuning, evaluating, and developing data collection/annotation/management tooling for LLMs

Skills

Required

  • Strong coding skills
  • solid understanding of algorithms and data structures
  • Proficient with various programming languages such as Go, Java, Python, TypeScript
  • Expert knowledge of API design and interface technologies (JSON, ProtoBuf, REST,RPC, XML, etc)
  • Experience with AWS Services such as Amazon S3, EC2, EKS / Kubernetes
  • Experience with event-based messaging systems (Kafka)
  • B.S, M.S. or PhD in Computer Science or equivalent experience

Nice to have

  • Experience with MongoDB and Unstructured data
  • Experience in Machine learning and/or Natural Language Processing
  • Experience of prompt engineering, fine-tuning, evaluating, and developing data collection/annotation/management tooling for LLMs
  • Exemplary ability to design, perform experiments, and influence engineering direction and product roadmap
  • Solid understanding of the software development process, including unit testing and release management

What the JD emphasized

  • working knowledge of machine learning concepts and systems
  • machine learning
  • Natural Language Processing
  • prompt engineering
  • fine-tuning
  • evaluating
  • developing data collection/annotation/management tooling for LLMs

Other signals

  • developing backend services
  • creating data and inference pipelines
  • enhancing search features using advanced machine learning methods
  • prompt engineering
  • fine-tuning
  • evaluating
  • developing data collection/annotation/management tooling for LLMs