Staff Generative AI Research Engineer, Reasoning & Memory - SIML

Cupertino, CA, US • Posted 10 hours ago • Updated 10 hours ago
Full Time
On-site
Fitment

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Job Details

Skills

  • Generative Artificial Intelligence (AI)
  • Operating Systems
  • Computer Hardware
  • Engineering Design
  • Fluency
  • Optimization
  • Reasoning
  • Evaluation
  • Workflow
  • MSC
  • Computer Science
  • Electrical Engineering
  • Mathematics
  • Physics
  • Computer Engineering
  • FOCUS
  • Modeling
  • Machine Learning (ML)
  • PyTorch
  • Research
  • Publications
  • Open Source
  • Algorithms
  • Training

Summary

The System Intelligence and Machine Learning (SIML) Content Understanding teams are seeking a Staff Applied Researcher in Reasoning & Memory Systems. You will be working alonside teams that are in charge of operating system wide embeddings, personalized RAG workstreams, tool calling, context compaction / efficiency & memory systems. Projects are focussed on advancing Apple Intelligence capabilities, while working closely across disciplines with our partners in hardware engineering, design and product. \\n\\nSelected references to our prior work (a) , (b) , (c) ;br>
We are seeking a candidate with a track record in algorithm development for agentic reasoning & memory. Key attributes expected in the role are fluency in algorithm development (prompt optimization and post training), strong expertise with relevant techniques (reinforcement learning, multimodal reasoning), and experience with automatic evaluation approaches for agentic workflow. \n\nThe role includes the opportunity to partner with world class system engineers to prototype and incorporate bleeding edge algorithmic innovations in the context of emerging agentic experiences. Ability to interface with large scale modeling & data infrastructure is desired.

PhD, or MSc in Computer Science/Electrical Engineering, or a related field (mathematics, physics or computer engineering); with a focus on machine learning, or comparable professional experience\nStrong ML and Generative Modeling fundamentals\nStrong expertise in one of the following: Reinforcement Learning, Multimodal Training, Pre-training / Post-training foundation models \nProficiency in using ML toolkits, e.g., PyTorch\nProven track record of research contributions demonstrated through publications in top-tier conferences, or open source contributions to algorithm

Experience with building & deploying Multimodal-LLMs\nFamiliarity with distributed training and large-scale data infrastructure
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 90733111
  • Position Id: 10e133ab66b5b4bdd25b7d1a3167fc41
  • Posted 10 hours ago
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