Top Machine Learning Engineers

As Senior ML Engineer, I lead the development of ML-based image editing system. I focus on combining technical skills with practical business insights to create tools that make a real difference. If you need someone who can transform data into meaningful results or build smart systems to improve your processes, let’s work together to make it happen.

After 18+ years in the wireless industry, where I worked in fraud analysis and sales, I made the leap into Data Science. After completing all coursework for my Master's in Data Science at CUNY Graduate Center, I gained hands-on experience during my internship at the CUNY Institute for State and Local Governance. Since then, I’ve started working at AIR Platforms, and I’ve expanded my expertise by developing AI-powered financial data extraction systems, processing PDFs and SEC filings to extract critical financial metrics. I’ve built robust pipelines to efficiently extract and update 20 years of financial data and implemented a system to evaluate and improve data extraction accuracy. Additionally, I’ve worked on user management and authentication systems in Python, further strengthening my ability to develop scalable, secure data solutions. I'm proficient in Python, C++, R, and SQL for data analysis and predictive modeling and am eager to continue leveraging my skills in a full-time Data Science role. My goal is to apply my experience in data extraction, pipeline development, and AI-powered automation to drive impactful insights in finance and beyond. References available upon request.

7 years of industry experience in machine learning engineering and research, including 3 years as an ML Engineering Manager and 4 years as an ML Engineer. As a manager, I built and scaled an ML organization from the ground up to ~30 engineers, leading projects from research through production. As an engineer, I designed and delivered end-to-end ML systems at scale, with expertise spanning model development, MLOps, infrastructure, and deployment. Experienced in bridging research and engineering to drive production-ready AI solutions.

Hi! I am a Machine Learning Research Leader at the University of California, Riverside. I have spent the past 7+ years mastering machine learning concepts and applying them to real-world engineering projects. I am passionate about statistics, machine learning, and data science, especially when making a tangible difference in the world. This is what drove me to get a Ph.D. in Electrical Engineering and Computer Science at the University of California, Riverside. Over the past seven years, I have used machine learning methods to solve problems across the United States Power Grid. I am proud to have worked on various topics, including Theft Detection, Anomaly Detection, Topology Classification, and Generating of Power System Data from Noise. In solving these problems, my team and I have used a heavy amount of machine learning and data analysis - usually relying on very large datasets. I have a solid knowledge of statistics and statistical machine learning theory, having invented and proven new theorems in Information-Theoretic Machine Learning Theory and Large Deviations Theory in statistics. I have mastered the art of building large-scale deep neural networks in both PyTorch and Tensorflow. My ability to produce and test more shallow learning methods via Scikit-Learn, Pandas and NumPy are also quite strong. I would say that I have a keen intuition for knowing when shallow, interpretable learning is more appropriate than deep. Most recently, I have led a research team consisting of myself and six graduate students through a two-year machine learning project solving major power system problems tasked by the U.S. Department of Energy. This research project has been quite successful, as we beat the performance of every other team involved! I am excited at the opportunity to expand my breadth of knowledge by working on challenging, novel problems. The beauty of machine learning is that, as the practitioner increases their breadth, their depth becomes stronger!

I am a Machine Learning Engineer at TikTok working on reducing misinformation and other harmful content using language modeling and graph based approaches. I have 3 years industry experience as an MLE at TikTok and 7 years academic experience as a PhD in Applied and Engineering Physics where I learned ML to solve research problems in quantum information / condensed matter.

I am currently an AI engineer at Prime Solutions Group in Goodyear Arizona. As an engineer I am part of a research team that focuses mainly on semi-supervised learning, specifically the positive-unlabeled problem. I received my bachelors in Computer Science and have a deep passion for Math, specifically Discrete Math and Probability Theory. As a professional, I am specifically interested in Machine Learning, Computer Vision and Algorithmic Design. Outside of school I am an avid aquatic athlete and was voted the captain of the University of Michigan's Club Water Polo team for three years straight.

I perform research towards future smart kitchens using machine learning/computer vision techniques coupled with novel data augmentation approaches.

As a machine learning engineer, I have a passion for developing innovative and scalable solutions that leverage the power of natural language processing and deep learning. I have 10+ years of experience in applying these techniques to various domains, such as document collaboration, content search, and user feedback analysis, at Dropbox, one of the leading cloud storage and collaboration platforms. At Dropbox, I was part of the machine learning team that worked on improving the quality and relevance of the search engine, as well as enhancing the features and functionality of the document editor. I used Python, Tensorflow, PyTorch, and PySpark to design, implement, and deploy machine learning models and algorithms that handled large volumes of text and image data. I also contributed to the development and maintenance of the machine learning infrastructure, using tools such as Airflow, Git, Bazel, AWS, GCP, SQL, and Kubernetes. I collaborated with cross-functional teams, including product managers, engineers, designers, and researchers, to deliver impactful and user-centric solutions.