Software Engineer · New York, NY
Nishant Dash
I build distributed systems, backend services, and developer tooling that brings value to any organization.
I recently finished an accelerated Master's in CS (4.0 GPA) and graduated Summa Cum Laude with a B.S.E. (3.94 GPA, completed in three years) at the University of Michigan. In three internships, I've shipped production barcode infrastructure, robotics diagnostics tooling, and open-source data platform code in industry. I'm now back at Capital One full time as an Associate Software Engineer.
Experience
2023 - 2025
2025
Migrated barcode generation for three production services (Java/Spring Boot, Java, and Python) onto a centralized Intelligent Mail Barcode (IMB) API. Built a Go-based API proof-of-concept using Ogen for OpenAPI codegen, instrumented with OpenTelemetry distributed tracing.
2024
Built 3D visualization, log-replay, and diagnostic tooling used across the team to debug and understand the behavior of warehouse robots in Symbotic's automated fulfillment systems.
2023
Contributed to iRODS, an open-source data management platform, working across the core codebase and surrounding tooling.
Education
ANN ARBOR, MI
- 01 Search engine built from scratch in C++ with a team of seven
- 02 Paxos consensus protocol implemented in Go
- 03 CNN written from scratch in CUDA
- 04 Sharded, consistent distributed key-value store
- 05 OS page system implementation
- 06 Network file system, built from the ground up
- 07 Research on adversarial training in NLP contexts
- James B. Angell Scholar - seven consecutive semesters, all A's
- Phillip Goldman Scholarship
- Lieutenant Francis Brown Lowry Scholarship
- Dean's List / University Honors - every semester, GPA above 3.5
Personal projects
I created this webpage from scratch, using many different languages to do so. The server runs on AWS Lambda functions written in Go.
A WiFi CSI-based fall detection pipeline built with Sam Desai and Kristine McLaughlin at the University of Michigan, running spectrogram classification on a Raspberry Pi.
A hierarchical caching system built with Arnav Reddy, Muzhe Wu, and Yuxuan Liu, at the University of Michigan that speeds up multimodal LLM inference by reusing responses and intermediate state across visually or semantically similar requests.