The premise of maximum likelihood estimation is that there is a probability distribution parameterized by variables. The goal is to find the variables that optimize the metric, which is the joint probability.
If you have a non-parametric model with no distribution, the metric is not a likelihood. In the case of k-means, it is some distance metric between your centers and data. So the terminology here is you are not doing a MLE, but minimization of a loss function.
The computational techniques to find optimal values are similar (eg. gradient descent algorithms) but MLE strictly refers to maximizing the defined joint probability.