Member of Technical Staff, MLE

Strong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions. Fluency with Python and core ML/LLM frameworks. Experience working with (or the ability to learn) large-scale datasets and distributed training or inference pipelines. Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies. Demonstrated ability to meaningfully shape LLM performance. A broad view of the ML research landscape and a desire to push the state of the art. Bias toward action, high ownership, and comfort with ambiguity. Humility and strong collaboration instincts.

Contribute to the design and delivery of custom LLM solutions for enterprise customers. Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies. Build custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines, and data assets. Develop SOTA modeling techniques that directly enhance model performance for customer use-cases. Contribute improvements back to the foundation-model stack. Work as part of Cohere’s customer facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact to our enterprise customers.