About Akshat Adsule
I'm a software engineer and UC Davis computer science and engineering graduate, working at Veeva (opens in a new tab) to build clinical data management software. My work spans production platforms, cloud systems, and hands-on engineering projects, including distributed deep rendering and compositing (opens in a new tab), where I'm building open-source tools for rendering, and orchestrating animation workloads. Before that, I built robots with my robotics team (opens in a new tab), launched an app at a large non-profit organization (opens in a new tab) for tracking horses in the wild, and worked at my school's IT department (opens in a new tab). Outside of software, I spend a lot of time with photography; some of my favorite shots live in my photo gallery (opens in a new tab).Software engineering experience
August 2026 - present Associate Software EngineerVeeva Systems (opens in a new tab)
- Building software for clinical data management.
June 2025 - Sept 2025 Software Engineering InternVeeva Systems (opens in a new tab)
- Worked on Veeva’s Electronic Data Capture (EDC) application, a platform for collecting, reviewing, and validating clinical study data
- Resolved 40+ software defects in a Java backend and React/Backbone frontend, improving system usability and reliability
- Implemented production features for file uploads, external data ingestion, and lab-data workflows in Veeva EDC, improving support for clinical study data collection.
- Java
- React
- JavaScript
- SCSS
- MySQL
- Git
- Jira
Oct 2023 - Jun 2026 Student Application DeveloperUC Davis IET (opens in a new tab)
- Collaborated with senior developers to maintain MyInfoVault, a platform for academic personnel
- Resolved defects and developed new features in a mature Java Spring web application as per the needs of actual users
- Collaborated on an ongoing UI refresh and transitioned existing JSP pages to modern technologies such as Vue.js
- Java
- Spring
- React
- MariaDB
- Jira
June 2023 - Sept 2023 Software Engineering InternAmerican Wild Horse Campaign (opens in a new tab)
- Worked with a team to build and release a full-stack mobile application to gather crowdsourced data to identify and tag horses in the wild with machine learning
- Designed and deployed backend systems for image processing, user management, and app functionality using standard technologies and platforms such as node.js, postgres, Microsoft Azure, Google Firebase, PostgresSQL, Docker, and Kubernetes.
- Implemented machine learning models into the backend to ensure image validity and quality while removing unwanted and dangerous content
- Node.js
- Postgres
- Azure
- Firebase
- Docker
- Machine Learning
Software projects
Jan 2026 - Present Distributed Deep Rendering and Compositing (opens in a new tab)
- Building Skewer, an open-source animation suite for distributed deep rendering, compositing, and render orchestration
- Developed a custom C++ ray tracing renderer with deep sampling support for per-pixel depth and opacity data
- Researched and implemented a deep image compositor for merging multi-layer render outputs efficiently
- Designed cloud infrastructure for distributed render jobs across Google Cloud Platform
- Created a React and Three.js scene previewer for editing scenes and dispatching render jobs
- C++
- Go
- React
- TypeScript
- Three.js
- GCP
- Terraform
Sep 2024 - May 2025 Volare (opens in a new tab)
- Launched Volare, a web-based AI interview coach that helps college students practice with role-specific mock interviews
- Developed a TypeScript backend and Next.js frontend for generating personalized interview sessions from job listings and user profiles and resumes
- Integrated ElevenLabs voice synthesis to support real-time conversational interview practice
- Added computer vision feedback for facial expression and emotion cues to make post-interview coaching more personalized
- TypeScript
- Next.js
- GCP
- Agents
- Large Language Models
April 2025 - June 2025 Bike Black Box (opens in a new tab)
- Built B3, a smart bike monitoring system for automatic ride tracking, crash detection, and live location sharing
- Prototyped embedded hardware with a TI CC3200 SoC, GPS antenna, OLED display, and accelerometer
- Used AWS IoT Device Shadows and SNS to synchronize bike state and send crash detection alerts in real time
- Developed a tracking web app and backend for ride history, live maps, and remote lock controls
- C++
- Go
- React
- Embedded Systems
- AWS IoT
- AWS SNS