AI Phone - Applied ML / Model Engineer

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