Multimodal Machine Learning Engineer, Global Siri

Apple Apple · Big Tech · Barcelona, Barcelona, Spain · Machine Learning and AI

ML Engineer for Apple's Global Siri team, focusing on building and scaling ML solutions across languages and modalities. The role involves working with large-scale AI models, agentic AI tools, and shipping ML-based products for worldwide customers.

What you'd actually do

  1. Work on cutting-edge technology at the intersection of agents, multilinguality and multimodality
  2. Understand how LLMs learn and transfer capabilities across languages and modalities
  3. Independently design and execute modeling experiments to validate training methodologies and model architectures
  4. Develop mechanisms to automate data creation and data filtering across different languages
  5. Devise automated approaches to evaluate and improve the performance of agents across multiple languages and modalities
  6. Build and ship ML based products at high quality for worldwide Apple customers

Skills

Required

  • Expertise in innovating with large-scale AI models (text, speech, and/or vision)
  • in-depth knowledge of the latest advancements in the field
  • deep understanding of LLM training, prompting, and fine-tuning
  • Experience with multilingual data, machine translation and understanding of the complexities and tradeoffs involved when scaling to non-English languages
  • Proficiency in Python
  • modern ML frameworks such as TensorFlow, PyTorch or JAX
  • Excellent interpersonal skills
  • work in a team as well as independently
  • can take and give feedback
  • can iterate on a solution in a collaborative environment

Nice to have

  • MS or PhD in Computer Science, Artificial Intelligence, Machine Learning or related field
  • Experience with multilingual and multimodal LLMs
  • Experience with or strong interest in agents, tool-use, planning and multi-step reasoning
  • Experience shipping production grade ML or DL based applications
  • Experience with building on-device software and models

What the JD emphasized

  • large-scale AI models
  • multilinguality
  • multimodality
  • agents
  • LLM training, prompting, and fine-tuning
  • multilingual data
  • non-English languages
  • Python
  • TensorFlow, PyTorch or JAX
  • shipping production grade ML or DL based applications

Other signals

  • large-scale AI models
  • multilinguality
  • multimodality
  • agents
  • production problems