Cohere Machine Learning Engineer Interview Questions

I would identify the slices where it fails, improve the training or retrieval data for those slices, and add targeted evaluation so the problem is visible in future releases. Average performance is not enough if the failures matter to users. In ML systems, the worst errors often matter more than the mean score.

I would look at model size, token usage, caching, batching, and whether a smaller or specialized model could do the job. The best optimization is often to simplify the workflow so the model is only used where it adds real value. Cost and latency usually improve together when the serving path is well designed.

I would minimize sensitive data use, control what gets logged, and make sure training and serving respect access policies. Privacy should be built into the system from the start because the data pipeline can be just as risky as the model. Good ML engineering treats privacy as a core requirement.

I would align on the research goal, product requirement, and operational constraint early. Researchers may care more about capability, while product engineers care more about stability, maintainability, and serving cost. Good collaboration means translating between those priorities without losing the technical truth.

I would use transfer learning, weak supervision, active learning, or data augmentation depending on the task. Limited labels make data quality and sample selection especially important, so I would focus on labeling the most informative examples first. The goal is to get the most learning value out of each label.