Travis B. Mitchell
Ph.D. Chemist & Software/AI Engineer
Open to full-time roles in Software/AI • Computational Chemistry • Synthesis/Materials R&D
Bridging atoms and bits to engineer the future of scientific software and the chemical sciences.
About Me
Ph.D. Chemist and full-stack engineer applying first-principles thinking across software/AI, computational chemistry, synthesis, and materials R&D. Experienced in production-grade software, AI/ML models, and automated scientific pipelines. Creator of ChemEonix, an independent R&D studio and portfolio where I explore causal approaches to machine learning and deep scientific insights. I am actively seeking a full-time role—whether in software/AI engineering, computational chemistry, synthesis/materials, or adjacent R&D—while remaining open to collaborations. Author of 18 peer-reviewed publications; strong scientific and technical communication, and cross-disciplinary teamwork.
Core Competencies
Experience leading projects from conceptualization to deployment, including architecture design, development, and mentorship. Built and delivered production-ready platforms at SparkAI, Reveel, and ChemEonix.
Deconstruct complex challenges to their scientific and engineering fundamentals and design novel solutions across software, chemistry, and process domains.
Identify high-value problems and translate deep scientific insights into strategic, fundable concepts and roadmaps.
Self-teach and master complex domains as needed, from modern full-stack development to quantum chemistry and machine learning.
18 peer-reviewed publications; experienced writing grant proposals, technical reports, and presenting complex findings clearly.
Work Experience
Architected and delivered a full-stack web platform for multi-objective formulation optimization. Developed a proprietary algorithm to automate neural network architecture search for sparse, high-dimensional data. Achieved 80% predictive accuracy, delivering a functional MVP that led to a formal case study with a major industrial partner.
Designed and built a fully autonomous quantum chemical pipeline to systematically generate, execute, and analyze vast computational chemistry libraries of diarylethene (DAE) molecules. The system was capable of autonomously executing and parsing thousands of calculations without human interaction.
Conducted doctoral research on photochromic materials and MOFs. Proactively designed and built a Python GUI application to streamline HPC workflows (Slurm/Gaussian) and redeveloped a legacy C++ tool into a modern Python application for high-throughput synchrotron data analysis.