Jonathon Byrd

I'm a machine learning engineer and applied researcher with a background in deep learning, control and decision-making, and real-world ML system design.

I’ve worked on applied ML at Google, published research at NeurIPS and ICML, and studied ML at the PhD level at Carnegie Mellon. My experience spans from core modeling and experimentation to scalable deployment, with a focus on techniques that support intelligent, adaptive systems.

🔹 Areas of focus include:

• Reinforcement learning, contextual bandits, and adaptive decision systems

• Deep learning (transformers, vision, multimodal models, generative modeling)

• Custom model architecture design, evaluation, and optimization

• ML infrastructure: MLOps, experiment tracking, deployment, monitoring

• Statistical modeling and probabilistic inference for causality, forecasting, and optimization

I’m especially interested in roles that balance research and engineering - solving open-ended problems, optimizing complex systems, or designing models that improve over time through feedback and interaction.

I bring a rigorous understanding of machine learning and strong execution on end-to-end systems, from exploratory modeling and architecture work to clean, production-grade pipelines. I'm currently open to contract or full-time remote opportunities on technical ML teams.