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Apple AIML - Machine Learning Engineer Foundation Model Services 
United States, California, Cupertino 
944938164

18.11.2024
Description
* Work along side Foundation Model Research team to prototype and develop inference for cutting edge model architectures. * Build tools to understand bottlenecks in Inference for different hardwares and use cases. * Mentor and guide engineers in the organization.
Minimum Qualifications
  • 8+ years of experience leading and driving complex, ambiguous projects.
  • Strong industry background and experience in ML technologies (LLMs, Machine Learning, NLP, Information Retrieval, Statistics).
  • Rich experience with high throughput services particularly at supercomputing scale.
  • Proficient with running applications on Cloud (AWS / Azure or equivalent) using Kubernetes, Docker etc.
  • Proficient in building and maintaining systems written in modern languages (eg: Golang, python)
Preferred Qualifications
  • Familiar with one of the popular ML Frameworks like Pytorch, Tensorflow.
  • Familiar with fundamental Deep Learning architectures such as Transformers, Encoder/Decoder models.
  • Familiarity with Nvidia TensorRT-LLM, vLLLM, DeepSpeed, Nvidia Triton Server etc.
Pay & Benefits
  • At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $175,800 and $312,200, and your base pay will depend on your skills, qualifications, experience, and location.Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
  • Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.