Machine Learning Research Engineer

AppleSan Diego, CA
1d

About The Position

We are seeking an experienced Machine Learning Research Engineer with a strong background in data curation and evaluation for Large Language Models. Your responsibilities will extend to the design and development of a comprehensive data selection, curation and annotation framework for foundation models at apple. We are looking for people passionate about innovating and applying ML algorithms that enable us to create high-quality and impactful datasets. As part of this role, you will also build evaluation benchmarks and evaluation methodologies to measure capabilities of existing models, work on data curation pipelines to hillclimb on benchmarks, and develop training frameworks for fine-tuning Foundation Models.

Requirements

  • Masters in Computer Science, Machine Learning or related field
  • Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning.
  • Deep understanding and expertise in foundation models.
  • Familiarity with emerging trends in generative AI and multi-modal LLM.
  • Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.

Nice To Haves

  • Familiarity with end-to-end Machine Learning Lifecycle
  • Demonstrated evidence of prior experience and expertise in data-centric machine learning research
  • Strong problem-solving and communication skills
  • Experience with recent post-training methodologies including reinforcement learning
  • Ph.D in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas

Responsibilities

  • design and development of a comprehensive data selection, curation and annotation framework for foundation models
  • build evaluation benchmarks and evaluation methodologies to measure capabilities of existing models
  • work on data curation pipelines to hillclimb on benchmarks
  • develop training frameworks for fine-tuning Foundation Models
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