About the role
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.
Originally posted on Himalayas