Summary
Saurabh Sawant is an Applied Scientist with seven years of engineering experience, blending academic rigor (MS CS, ASU, 4.0 GPA) with production-grade ML and cloud engineering across Microsoft, AWS, Amazon, and Wipro. He builds scalable ML systems and ETL pipelines—evidenced by an ML routing system that cut incorrect routing by 18% and a Spark-based pipeline that reduced processing time on TB-scale Mastercard data by 40%. His research output includes VarBERT, a state-of-the-art approach to recovering variable names from decompiled code (50.7% accuracy) and efficiency gains for Seq2Seq models achieving comparable results with just 10% of training data. Comfortable from prototyping to deployment, he has implemented CI/CD for interactive data extraction on AWS and written tooling to simplify SageMaker access for researchers. A mentor and pragmatic collaborator, he pairs deep learning expertise (PyTorch/TensorFlow) with cloud-native practices to deliver measurable improvements in accuracy, latency, and developer productivity. Based in Seattle, he looks for roles that bridge advanced research and real-world ML systems.
7 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Computer Science, 4/4, Master of Science - MS, Computer Science, 4/4 at Arizona State University
Bachelor of Technology - BTech, Computer Science, 8.41/10, Bachelor of Technology - BTech, Computer Science, 8.41/10 at Vellore Institute of Technology