AI Phone - Applied ML / Model Engineer
Bjak 路 United States
Extracted from the posting
Applied ML / Model Engineer role evaluating open-weight foundation models (Qwen, Llama, Gemma) against phone use cases and deciding where fine-tuning is needed. Work includes SFT, LoRA/QLoRA, synthetic dataset construction, and building evaluation benchmarks for intent recognition, tool calling, and multi-step tasks. Collaborates with the Agent Runtime team and uses PyTorch.
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.
