John Parker, PhD

Leading software engineering and data platform strategy at a biotech startup, aligning cloud infrastructure and AI initiatives with business goals

• Authored company-wide SOPs for lab data storage, sequence database ingestion, and software engineering best practices
• Integrating Agentic AI systems into development workflows and data platforms using Claude Code, AWS Bedrock, and Databricks Genie
• Building a strong engineering team through project ownership, skill development, and alignment with business priorities

Managed a software engineering team at a biotech startup, overseeing cloud-based deep learning and bioinformatics workflows while providing platform support across data scientists, lab scientists, and leadership

• Led company-wide migration to the Databricks platform, centralizing data and enabling self-service analytics
• Partnered with IT to enable real-time lab data ingestion of many instruments using AWS S3 File Gateway and Spark Declarative Pipelines
• Transitioned team focus from infrastructure maintenance toward application and data engineering with direct business impact

Developed and maintained a greenfield machine learning software platform on AWS to enable and support Data Science workloads

• Architected greenfield ML platform on Kubernetes (EKS) with Metaflow and Argo Workflows, supporting parallel GPU training jobs for VAE and transformer models
• Unified ML/SWE repositories into a trunk-based monorepo with GitHub Actions CI/CD, merge queues, and feature flags for continuous deployments
• Migrated data warehouse to Apache Iceberg lakehouse with FastAPI/Panel stack for dataset streaming and cross-team data sharing
• Partnered with NVIDIA as early access collaborator to integrate BioNeMo T5 LLMs into custom VAE architecture on Kubernetes.

Provided software development, training, and system administration for a Linux-based high performance computing (HPC) cluster serving University researchers

• Benchmarked CPU and GPU performance on next-gen HPC cluster with NVIDIA A100 (MIG), AMD, and Intel hardware using CUDA and MPI.
• Built multi-GPU molecular dynamics framework using JAX and MPI, scaling ensemble simulations beyond single-node limits.
• Led interactive HPC workshops on SLURM, parallel Python/MPI, and high-performance computing concepts.

Conducted computational physics research spanning simulation development, software publication, and academic scholarship.

• Published cross-platform Python & C++ simulation packages for electrodynamics and hydrodynamics, using CMake and pybind11
• Co-authored over a dozen peer-reviewed publications with collaborators at the University of Chicago, Argonne National Laboratory, and partner academic institutions.

PhD Physics

Computational physics research in Norbert Scherer's Lab. Thesis "Collective Electrodynamic Excitations and Non-conservative Dynamics in Optical Matter and Meta-atom Systems"

BS Physics, Minor in Mathematics

Computational physics research in Daniel Goldman's Complex Rheology And Biomechanics (CRAB) lab, studying the physical properties of granular materials.