Description review
AI Phone - Applied ML / Model Engineer
Bjak · Remote - Seoul, Korea · back to the listing
HR standards
65/100
needs work
Title ↔ description
79/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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Startups
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What the listing never says
- 18 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
About
We are reimagining the phone. AI phones need to be proactive, intuitive, intelligent and low cost. This is a mission critical for rest of the world to adopt intelligence.
There are over 5 billion people using everyday applications such as email, messaging, notes, tasks, and calendars, yet most are not AI-native. Our mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination and workflows, with minimal prompting.
Responsibilities
• Evaluate existing foundation models (e.g. Qwen, Llama, Gemma and other suitable open-weight models) against our target phone use cases.
• Identify which capabilities can be handled by existing models and where additional model adaptation or fine-tuning is needed.
• Experiment with SFT, LoRA/QLoRA and other lightweight fine-tuning techniques for specific use cases.
• Improve model performance for intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.
• Explore synthetic data generation and dataset construction for phone-specific workflows.
• Build evaluation datasets and benchmarks to measure model quality, reliability, and task completion.
• Compare different models and approaches, including local versus cloud inference.
• Work closely with the Agent Runtime team to improve model performance in actual agent workflows.
Requirements
• Strong background in Machine Learning, Deep Learning, or NLP.
• Practical experience working with LLMs and open-weight models.
• Experience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.
• Experience with PyTorch and common ML tooling.
• Understanding of LLM evaluation, benchmarking, and dataset preparation.
• Ability to run experiments, analyze model failures, and translate findings into measurable improvements.
Nice to have
• Experience with agentic systems, function calling, and tool-use training.
• Experience with synthetic data generation and preference datasets.
• Experience with model distillation and smaller language models.
• Experience with deploying models in production environments.
We are reimagining the phone. AI phones need to be proactive, intuitive, intelligent and low cost. This is a mission critical for rest of the world to adopt intelligence.
There are over 5 billion people using everyday applications such as email, messaging, notes, tasks, and calendars, yet most are not AI-native. Our mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination and workflows, with minimal prompting.
Responsibilities
• Evaluate existing foundation models (e.g. Qwen, Llama, Gemma and other suitable open-weight models) against our target phone use cases.
• Identify which capabilities can be handled by existing models and where additional model adaptation or fine-tuning is needed.
• Experiment with SFT, LoRA/QLoRA and other lightweight fine-tuning techniques for specific use cases.
• Improve model performance for intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.
• Explore synthetic data generation and dataset construction for phone-specific workflows.
• Build evaluation datasets and benchmarks to measure model quality, reliability, and task completion.
• Compare different models and approaches, including local versus cloud inference.
• Work closely with the Agent Runtime team to improve model performance in actual agent workflows.
Requirements
• Strong background in Machine Learning, Deep Learning, or NLP.
• Practical experience working with LLMs and open-weight models.
• Experience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.
• Experience with PyTorch and common ML tooling.
• Understanding of LLM evaluation, benchmarking, and dataset preparation.
• Ability to run experiments, analyze model failures, and translate findings into measurable improvements.
Nice to have
• Experience with agentic systems, function calling, and tool-use training.
• Experience with synthetic data generation and preference datasets.
• Experience with model distillation and smaller language models.
• Experience with deploying models in production environments.