Time since we first saw it
Day 2
Early. Similar jobs usually come down between day 6 and day 71.
At Bjak, jobs usually come down by about day 45.
From 4,624 similar postings that came down, and 9 of Bjak’s. Coming down is not always a hire.
AI Phone - Applied ML / Model Engineer
Seoul, Korea · Seoul, South Korea
Actively hiring, first seen by us 2 days ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
ActAIealware — team source
Department: ActAIealware
From the employer’s posting
What you’ll work on
Full postingEvaluate open-weight foundation models against target phone use cases
Fine-tune models with SFT, LoRA/QLoRA and other lightweight methods
Improve intent recognition, instruction following, and tool calling
Build synthetic datasets for phone-specific workflows
Build evaluation datasets and benchmarks for model quality and reliability
From the employer’s posting
Evaluate existing foundation models (e.g. Qwen, Llama, Gemma and other suitable open-weight models) against our target phone use cases.
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.
What you’ll bring
All qualificationsCore experience
- Practical experience working with LLMs and open-weight models.
- Experience with agentic systems, function calling, and tool-use training.
- Experience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.
- Experience with synthetic data generation and preference datasets.
- Experience with PyTorch and common ML tooling.
- Experience with model distillation and smaller language models.
Qualification wording
Practical experience working with LLMs and open-weight models.
Experience with agentic systems, function calling, and tool-use training.
Experience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.
Experience with synthetic data generation and preference datasets.
Experience with PyTorch and common ML tooling.
Experience with model distillation and smaller language models.
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 4, 20262 days ago
- Recorded sightings
- 2
- Last seen by us
- Nov 5, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Bjak is hiring
85 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is steady
- Its jobs tend to stay up a long time
- 6% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- PyTorch
Where the posting names them
- 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.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Has Bjak posted this job before?
Yes. We’ve followed 5 other AI Phone - Applied ML / Model Engineer jobs there since November 2026. All 5 are still up.
Large employers often post one title for several teams, so these can be separate openings.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Seoul, South Korea
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.