Blake Moore, PhD
About Blake Moore, PhD
Blake Moore, PhD, is a Staff Machine Learning Engineer at Flock Safety, specializing in audio machine learning and high performance computing.
Title and Current Role
Blake Moore, PhD, holds the position of Staff Machine Learning Engineer at Flock Safety. In this role, Moore leads a team responsible for training, deploying, and iterating on audio machine learning models and systems. The responsibilities also include overseeing the integration of edge devices, cloud services, and system-level logic to directly serve end users.
Professional Experience at Flock Safety
Moore has a diverse background at Flock Safety, beginning as a Machine Learning Engineer from 2022 to 2023, then advancing to Senior Machine Learning Engineer from 2023 to 2024. Each position provided Moore with the opportunity to apply and refine skills in machine learning, focusing on audio applications. The progression in roles highlights a trajectory of growth and increased responsibility within the company.
Experience at Raytheon Technologies
Before joining Flock Safety, Moore worked as a Simulation and Machine Learning Engineer at Raytheon Technologies from 2020 to 2022. This position involved applying machine learning techniques to simulation problems, an experience that further enriched Mooreβs proficiency in both theoretical and practical aspects of machine learning.
Educational Background
Blake Moore earned a PhD in Physics from Montana State University-Bozeman, completing the program from 2016 to 2020. During this time, Moore also worked as a Graduate Research Assistant, engaging in extensive research activities. Additionally, Moore holds a Master's degree in Physics from the same institution (2016-2018), and a Bachelor's degree in Physics from Montclair State University (2012-2016).
Technical Expertise
Moore specializes in audio machine learning and has a strong interest in Bayesian/causal inference and Markov Chain Monte Carlo (MCMC) methods. Proficient in Python, Moore utilizes frameworks such as TensorFlow, PyTorch, and PyMC for machine learning tasks. Additionally, Moore is experienced in high performance computing, leveraging both CPU and GPU resources to optimize computational efficiency.