AI Phone - Applied ML / Model Engineer
China · India · Seoul, Korea · Taipei, Taiwan · United Kingdom · United States · 4 days ago
You will evaluate open-weight foundation models against phone use cases, fine-tune them with SFT and LoRA/QLoRA, and build evaluation datasets and benchmarks. You will also work with the Agent Runtime team to improve model performance in agent workflows.
- Workplace
- Remote
- Team
- ActAI Realware
Skills
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.
Posted Nov 5, 2026