AI Phone - Applied ML / Model Engineer

Bjak • Madison, MS

2d

About The Position

The Applied ML / Model Engineer will help develop proactive, intuitive, intelligent, and low-cost AI phones. The role focuses on evaluating foundation models for phone use cases, determining where existing capabilities are sufficient and where adaptation or fine-tuning is needed, and improving model performance for phone-specific workflows. The engineer will build evaluation datasets and benchmarks, compare local and cloud inference approaches, and collaborate 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, such as SFT and LoRA/QLoRA; RL or preference optimization experience is preferred.
• 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 Haves

• 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.

Responsibilities

• Evaluate foundation models, including Qwen, Llama, Gemma, and other suitable open-weight models, against target phone use cases.
• Identify which capabilities existing models can handle and where model adaptation or fine-tuning is needed.
• Experiment with SFT, LoRA/QLoRA, and other lightweight fine-tuning techniques for specific use cases.
• Improve performance in 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 versus cloud inference.
• Work with the Agent Runtime team to improve model performance in actual agent workflows.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed