Alec Hoyland is a Senior Machine Learning Engineer with eight years of experience bridging computational neuroscience and applied AI, currently building trustworthy, formally constrained LLM systems at Jaxon.AI. He has led R&D and production deployments for federal and public-sector customers, specializing in fine-tuning, quantization, dataset curation, and metrics optimization for large language and vision models. Alec’s background in neuroengineering and signal processing informs robust approaches to noisy real-world data—he’s developed VAEs/GANs for radar denoising and unsupervised preprocessing that boosted detection AP by ~35%. He has a track record of technical leadership on multi-million dollar projects, proposal writing, and accelerating research codebases (including 100x speedups for neural data pipelines). Based in Worcester, MA, Alec combines deep scientific rigor from graduate neuroscience work with pragmatic ML engineering to make mission-critical AI both performant and auditable. An engineer who learned his craft modeling neural circuits, he brings an uncommon blend of theoretical insight and production-first delivery.
8 years of coding experience
11 years of employment as a software developer
Master of Science - MS Neuroscience, Master of Science - MS Neuroscience at Brandeis University
Doctor of Philosophy - PhD (Incomplete) Biomedical Engineering, Doctor of Philosophy - PhD (Incomplete) Biomedical Engineering at Worcester Polytechnic Institute
Bedford High School
Non-Degree Student Engineering, Non-Degree Student Engineering at Boston University
Code implements the LN model used to describe spike trains of MEC neurons based on the animal's position, head direction, speed, and theta phase information. Used in Hardcastle et al., 2017.
Contributions:10 PRs, 81 pushes, 8 branches in 8 months
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Alec Hoyland - Senior Machine Learning Engineer at Jaxon.AI