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.

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