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TETether Operations Limited

.AI Research Engineer (Model Compression & Quantization)(m/w/x)

Zürich
Full-timeRemoteSenior
AI/ML

Applying low-bit quantization to generative AI models for financial token integration. PhD in NLP/ML and A* publications required. Focus on model compression and efficient deployment.

Requirements

  • Degree in Computer Science or related field
  • PhD in NLP, Machine Learning, or related field
  • Solid track record in AI R&D with A* publications
  • Experience with PyTorch or equivalent frameworks
  • Hands-on experience with model quantization (QAT and PTQ)
  • Research and hands-on experience with knowledge distillation
  • Research and hands-on experience with model pruning
  • Solid understanding of neural network architectures and training
  • Understanding of transformers (LLMs, VLMs), backpropagation, optimization, fine-tuning
  • Familiarity with C++ (advantageous)

Tasks

  • Drive innovation in model compression and efficient deployment
  • Reduce model footprint and computational cost
  • Apply low-bit quantization to generative AI models
  • Maintain accuracy and output quality during quantization
  • Leverage knowledge distillation for efficient multimodal reasoning
  • Implement pruning techniques to reduce computational overhead
  • Analyze trade-offs between model efficiency and accuracy
  • Propose improvements based on empirical findings
  • Research mixed-precision quantization and advanced compression strategies
  • Stay current with the latest research in model compression
  • Document methodologies, experiments, and results clearly
  • Support reproducibility and internal collaboration
  • Communicate results to stakeholders
  • Author technical papers and publish findings
  • Advance the field of model compression for multimodal AI

Work Experience

  • approx. 4 - 6 years

Education

  • Bachelor's degree

Languages

  • EnglishBusiness Fluent

Tools & Technologies

  • PyTorch
  • model quantization
  • Quantization-Aware Training (QAT)
  • Post-Training Quantization (PTQ)
  • knowledge distillation
  • model pruning
  • neural network architectures
  • transformers
  • LLMs
  • VLMs
  • backpropagation
  • optimization
  • fine-tuning
  • C++
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