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AI Phone - Applied ML

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AI Phone - Applied ML in Others new

  • Seoul, Korea

Responsibilities

  • Evaluate Qwen, Llama, Gemma, and other open-weight foundation models for AI phone use cases.
  • Identify when existing models are sufficient and when model adaptation or fine-tuning is required.
  • Experiment with SFT, LoRA, QLoRA, and other lightweight fine-tuning techniques.
  • Improve model performance for intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.
  • Explore synthetic data generation and construct datasets for phone-specific workflows.
  • Build evaluation datasets and benchmarks for model quality, reliability, and task completion.
  • Compare models and inference approaches, including local and cloud inference.
  • Collaborate with the Agent Runtime team to improve model performance in agent workflows.
  • Run experiments, analyze model failures, and translate findings into measurable improvements.

Requirements

  • Strong background in machine learning, deep learning, or NLP.
  • Practical experience working with LLMs and open-weight models.
  • Experience with model fine-tuning techniques such as SFT, LoRA, and QLoRA; RL or preference optimization experience is preferred.
  • Experience with PyTorch and common machine learning tooling.
  • Understanding of LLM evaluation, benchmarking, and dataset preparation.
  • Ability to run experiments, analyze model failures, and produce measurable improvements.
  • Experience with agentic systems, function calling, tool-use training, synthetic data generation, preference datasets, model distillation, smaller language models, or production model deployment is preferred.

Bjak

Our mission is to develop technology based solutions to improve financial inclusion. We develop new & innovative platforms & services globally. For example, we are the first platform to simplify and digitise comprehensive life and medical insurance, supported by AI agent. BJAK is the largest insu...

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