Staff Applied Scientist

Summary

I am an AI researcher and engineer with over six years of experience in applied machine learning, focused on robust modeling, anomaly detection, and large-scale AI systems. My work spans both algorithm development and end-to-end system design, emphasizing models that remain reliable under distribution shift, noise, and adversarial conditions.

At NetApp, I led the development of an AI-powered detection system, where I designed and evaluated machine learning models, conducted rigorous experimentation, and deployed production-grade pipelines. This work integrates research-driven modeling with real-world system constraints and has contributed to multiple patent applications in machine learningbased detection and robust AI system design.

Beyond model development, I have built machine learning infrastructure including training pipelines, evaluation frameworks, and AI systems for continuous model iteration and deployment across distributed environments. My work bridges algorithmic research with system-level implementation, focusing on reliability, scalability, and robustness.

My background includes large-scale software systems and ML-related infrastructure, which complements my work in applied machine learning and supports building production-ready AI systems. My research interests include representation learning, foundation model robustness, adversarial machine learning, and agent-based systems.

Expectations

I am looking for a role where I can contribute to impactful machine learning research and engineering work at scale, especially problems that sit at the intersection of model development and real-world system constraints. I am particularly interested in opportunities to take ideas from research through rigorous experimentation, evaluation, and into production-grade deployment.

I also value working with strong research and engineering teams where I can develop my understanding of modern machine learning, including representation learning, robust and adversarial methods, and large-scale AI systems. In addition, I hope to have end-to-end ownership of meaningful technical problems and continue growing both my research depth and system-building capabilities.

Employment Preferences

Expected Base Salary

**5,000 USD

Academic Degree

Experience

Skills

• Python
• R
• Java J2EE
• Up To JDK 1.8
• JavaScript
• Shell
• C
• C++
• Octave
• SQL Server 2000
• 2016 + T-SQL
• MongoDB CDC
• Minio
• Oracle9i
• 10g + PL-SQL
• SQLite
• MySQL
• Lasso
• Ridge
• Elastic Net
• Logistic
• Censored Regression
• SVM
• RBF Kernel
• Ada Boosting
• Multinomial Naive Bayes Classification
• Random Forest
• Gradient Boosting Machine
• K Nearest Neighbors
• Latent Dirichlet Analysis
• Artificial Neural Networks
• Recurrent Neural Networks
• LSTM
• GRU
• Convolutional Neural Networks
• Generative Adversarial Networks
• Reinforcement Learning
• Auto Encoder
• Transformer
• Attention Network
• Transfer Learning
• Sobel Filter
• Hough Transform
• L-BFGS-B
• Principal Component Analysis
• PySpark
• Sklearn
• SciPy
• PyTorch
• Tensorflow2
• NLTK
• SpaCy
• BERT
• Pandas
• NumPy
• Plotly
• Matplotlib
• Selenium
• BeautifulSoup
• Django
• Flask
• Spring Boot
• Netty
• NIO
• AIO
• Jersey2
• Gradle
• Djongo
• Hibernate
• MyBatis
• IBatis
• JDBC
• JUnit
• Mockito
• PowerMock
• Crystal Report2008
• EJB2.0
• Struts2
• Struts1
• Jackson
• POI
• HTML5
• Ajax
• Bootstrap2
• FabricJS
• Backbone
• RequireJS
• JQuery
• Spark
• Apache Kafka
• OpenMQ
• Websphere6
• Weblogic10g
• Tomcat5.5
• Jetty
• SOAP
• Restful
• Domain Driven Design
• Object Orient Design
• Agile
• Microservice