Research Engineer

Role overview Improve the quality and operational behavior of open language models serving production workloads. The work spans model routing, fine-tuning, alignment, and evaluation, with an emphasis on turning research into measurable improvements in quality, latency, and cost.

Responsibilities - Develop and evaluate strategies for routing requests across an open-model pool, measuring effects on quality, response time, and cost. - Design fine-tuning and alignment approaches—including supervised fine-tuning, LoRA or QLoRA, preference optimization, and continued pre-training—for dedicated GPU environments. - Build evaluation systems that identify regressions before they affect users. - Review research, reproduce promising methods, and move effective results into practice. - Work with product engineers to make research results visible and controllable in product interfaces. - Document findings in internal notes, product documentation, and occasional public writing.

Requirements - At least five years in machine learning research or engineering, including substantial work with large language models. - Practical experience fine-tuning open-weight models using PyTorch, distributed training, and parameter-efficient techniques. - Strong evaluation discipline and the ability to demonstrate evidence for claims of model improvement. - Engineering practices that make experiments reproducible by others.