AI Phone - Applied ML / Model Engineer
- Location
- Remote (US)
- Listed
- Today
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.
From Bjak's careers page, first listed on Rolev.ai on November 10, 2026. Rolev.ai is not the employer.
How Rolev.ai helps with this application
Rolev.ai is a job search and application tool for US job seekers. For this AI Phone - Applied ML / Model Engineer role at Bjak, it shows how your resume fits, tailors your resume to the posting, and prepares the application for you to review.
See how you fit
Openings from employer career sites are ranked against your whole resume—and searchable inside Claude, ChatGPT, and Cursor via Rolev.ai MCP.
Tailor without inventing
Every change to your resume is checked against your original. New experience goes in only with your yes—zero fabricated claims.
Draft the full kit
Tailored and Full Match resumes, role-specific cover letters, and answers to employer screening questions are prepared in seconds.
1-Click Extension Autofill
The Chrome extension fills the employer's Greenhouse, Workday, Lever, or Ashby form in 1 click. You review it and press Submit.
Common questions
Where is the AI Phone - Applied ML / Model Engineer job at Bjak located?
Yes, this AI Phone - Applied ML / Model Engineer opening at Bjak is listed as Remote (US). Always check Bjak's application page for specific state eligibility or relocation policies.
How do I apply for the AI Phone - Applied ML / Model Engineer position at Bjak?
This listing links directly to Bjak's Ashby application page. You can apply directly on Bjak's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Ashby application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.