AI Phone - Applied ML / Model Engineer

AboutWe 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. ResponsibilitiesEvaluate 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. RequirementsStrong background in Machine Learning, Deep Learning, or NLP. Practical experience working with LLMs and open-weight mode