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ML Engineer, Model Compression

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ML Engineer, Model Compression in Others new

  • Helsinki, Finland

Responsibilities

  • Apply quantisation, pruning, and knowledge distillation to real models and recover accuracy through fine-tuning, retraining, or other methods.
  • Profile model memory, bandwidth, and compute usage and convert findings into optimization decisions.
  • Optimize models for hardware accelerators including GPUs, NPUs, and TPUs.
  • Deploy production-ready models in client environments and QuTwo OS.
  • Build domain adaptation harnesses involving retrieval pipelines, tool integrations, structured prompting, or agent loops.
  • Work with researchers, engineers, and client teams to move research systems into production.
  • Use Python for research code and model artifacts and C/C++ for low-level optimization.

Requirements

  • Practical experience applying model compression techniques such as quantisation, pruning, and knowledge distillation.
  • Experience profiling models and optimizing them for specific hardware accelerators.
  • Fluency with current open-weight model families and their tooling.
  • Experience building domain adaptation harnesses and deciding when they are preferable to fine-tuning.
  • Proficiency in Python and C/C++.
  • Several years of hands-on experience shipping models to production in industry or an applied research lab.
  • Ability to work across changing projects and hardware stacks and own work through deployment.

Qutwo

Qutwo builds enterprise AI and quantum-computing solutions, combining its Qutwo OS, ML pipelines for neural network compression, and security-focused SaaS services for industrial optimization, simulation, and analytics. The company partners with enterprises to develop classical, hybrid, and quant...

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