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# ML / Model Engineer

- **Company:** ONE STOP COLLECTIBLE CORP
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Data Files, Machine Learning, Language Modeling, QLoRA, Pytorch, Large Language Models, Deep Learning, Machine Learning Operations, Evaluation of Large Language Models, Invoking Functions, Data Generation
- **Published:** November 5, 2026
- **Apply:** https://startup.jobs/ai-phone-applied-ml-model-engineer-bjak-my-10677337

## About the Role

* 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.

## Description

* 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.