AI Phone - Applied ML / Model Engineer
Bjak
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- Location
- United States
- Work style
- Remote only
- Job type
- Full time
- Posted
- Nov 4
- 4 days ago
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Career path
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About the company
Bjak
Computer software · 501 to 1,000 employees
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.
What you'll do
- Evaluate existing foundation models against target phone use cases
- Identify where model adaptation or fine-tuning is needed
- Experiment with SFT, LoRA/QLoRA and other lightweight fine-tuning techniques
- Improve model performance for intent recognition, instruction following and tool calling
- Explore synthetic data generation and dataset construction for phone-specific workflows
- Build evaluation datasets and benchmarks
- Compare different models and approaches, including local versus cloud inference
- Work closely with the Agent Runtime team to improve model performance in agent workflows
What you need
- Strong background in Machine Learning, Deep Learning, or NLP
- Practical experience working with LLMs and open-weight models
- 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 improvements
- Experience with RL or preference optimization (preferred)
- Experience with agentic systems, function calling, and tool-use training (preferred)
Key skills
Machine LearningDeep LearningNLPLLMsopen-weight modelsSFTLoRA/QLoRAPyTorchLLM evaluationbenchmarkingdataset preparationRLpreference optimization
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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.