Luke Martin

• Architected automated protein design pipelines integrating RFAntibody, Bindcraft, and internal methods — deployed as Docker containers on a Kubernetes cluster with Redis queuing and dynamic resource scaling. • Ported several recent DL research papers into the internal codebase, replacing non-commercial components with licensed equivalents.

• Integrated diffusion-based protein generative models into the internal generation system; fine-tuned with classifier-free guidance for conditional generation without degrading benchmark performance. Built in PyTorch and PyTorch Lightning. • Designed Ordaos' core protein optimization platform — combining structure prediction, interaction scoring, and numerical property models to drive design decisions. Used to achieve a 10x binding improvement on a SARS-CoV-2 antibody while maintaining resilience to target mutation.

• Built components of a multi-modal protein attention model in PyTorch, predicting sequence, atomic positions, and biophysical properties. • Designed and trained a log Kd model for antibody-antigen binding affinity prediction. After fine-tuning on internal data, the model ranked prospective antibodies such that 57 successful binders fell within the top 70 candidates.

• Built an Azure microservice to continuously ingest new arXiv papers into an internal database. • Embedded paper abstracts with BERT and built a similarity-based recommendation system to surface relevant literature.

• Built Keras/TensorFlow models to forecast regional cash-flow requirements across the South African Reserve Bank's network, outperforming the existing baseline by up to 40%.