  Bayesian optimization (BO) provides a powerful framework for optimizing
black-box, expensive-to-evaluate functions. It is therefore an attractive tool
for engineering design problems, typically involving multiple objectives.
Thanks to the rapid advances in fabrication and measurement methods as well as
parallel computing infrastructure, querying many design problems can be heavily
parallelized. This class of problems challenges BO with an unprecedented setup
where it has to deal with very large batches, shifting its focus from sample
efficiency to iteration efficiency. We present a novel Bayesian optimization
framework specifically tailored to address these limitations. Our key
contribution is a highly scalable, sample-based acquisition function that
performs a non-dominated sorting of not only the objectives but also their
associated uncertainty. We show that our acquisition function in combination
with different Bayesian neural network surrogates is effective in
data-intensive environments with a minimal number of iterations. We demonstrate
the superiority of our method by comparing it with state-of-the-art
multi-objective optimizations. We perform our evaluation on two real-world
problems -- airfoil design and 3D printing -- showcasing the applicability and
efficiency of our approach. Our code is available at:
https://github.com/an-on-ym-ous/lbn_mobo
