AI Phone - Applied ML / Model Engineer
Bjakcareer · United States
About this role
## AI Phone — Applied ML / Model Engineer ### About We’re reimagining the phone. AI phones need to be proactive, intuitive, intelligent, and low cost—so the rest of the world can adopt intelligence. While billions use everyday apps (email, messaging, notes, tasks, calendars), most aren’t 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 open-weight options) against target phone use cases - Determine which capabilities can be handled by existing models vs. where adaptation/fine-tuning is needed - Experiment with SFT, LoRA/QLoRA, and other lightweight fine-tuning techniques for specific use cases - Improve 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 models and approaches, including local vs. cloud inference - Partner with the Agent Runtime team to improve model performance in real 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 (SFT, LoRA/QLoRA; 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 deploying models in production environments
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