Generative AI Applied Scientist, Siml - Ise

Apple Apple · Big Tech · Cupertino, CA +1 · Machine Learning and AI

Seeking a senior Generative AI expert to pioneer next-gen human-centric device interaction and multimodal scene understanding. Focus on end-to-end lifecycle of multimodal LLMs, from architecture design and large-scale training to final deployment.

What you'd actually do

  1. Design, train, and deploy large-scale multimodal LLMs, owning the entire lifecycle from initial architecture to final deployment within the constraints
  2. Implement and demonstrate novel, human-centric user experiences by applying the capabilities of large foundation models
  3. Create robust, scalable ML models and APIs that can be well-integrated into Apple's production pipelines and training infrastructure
  4. Work closely with partner teams to build, iterate, and adapt innovative solutions in a dynamic, product-focused environment

Skills

Required

  • PhD or Masters Degree in Computer Science, Engineering, or a related field with a focus on machine learning; or equivalent experience
  • Strong research skills with first author publications in top tier ML conferences
  • Expert-level knowledge of SOTA in large auto-regressive transformer models, multi-modal encoders, and representation learning
  • Experience with multimodal large language models (LLMs)
  • Strong programming skills in Python, maintaining ML code bases grounded in software engineering principles

Nice to have

  • Familiarity with developing ML for resource-constrained devices
  • Experience working with large cross-functional and diverse teams

What the JD emphasized

  • first author publications in top tier ML conferences
  • Expert-level knowledge of SOTA in large auto-regressive transformer models, multi-modal encoders, and representation learning
  • Experience with multimodal large language models (LLMs)
  • Strong programming skills in Python, maintaining ML code bases grounded in software engineering principles
  • Proven track record of deploying innovative ML technologies in production

Other signals

  • multimodal LLMs
  • large-scale training
  • deployment
  • human-centric device interaction
  • scene understanding