Hi, I’m Atharv Chandratre.
I'm a forward-thinking Software Engineer with a passion for solving challenging problems in collaborative environments.
I'm a Software Engineer II at DoorDash in San Francisco, building scalable distributed services and ML-enabled data pipelines. I hold an M.S. in Computer Science (Machine Learning) from UIUC—alongside experience at Goldman Sachs and an Amazon internship.
I am driven by the challenge of innovating in the tech landscape, and I am always eager to apply my expertise to new, groundbreaking projects.
About me
I am open to new opportunities for full-time positions as a software developer. Feel free to contact me if you have any!
I work on backend and data systems at DoorDash, building scalable distributed services and shipping reliable ML-enabled pipelines. I also hold an M.S. in Computer Science (Machine Learning) from UIUC. I enjoy applying ML where it improves real system decisions.
My favorite part of programming is the problem-solving aspect. I love the feeling of finally figuring out a solution to a problem. I am always looking to learn new technologies. I am also a tinkerer at heart. I try to create enjoyable solutions that automate life's boring parts.
In my free time, I play badminton, do origami and make latte art.
Education
University of Illinois Urbana-Champaign
M.S. in Computer Science
Aug 2022 — May 2024
Specialization in Machine Learning
Birla Institute of Technology and Science, Pilani
B.E. in Computer Science
Aug 2017 — Jun 2021
My experience
DoorDash - Software Engineer II
San Francisco, CA
- Migrated a ~1,000 QPS authorization/permission service from Elasticsearch to CockroachDB, cutting latency 62% (351ms → 131ms) and eliminating a recurring incident class tied to substantial ads revenue risk.
- Architected an Elasticsearch source-of-truth + distributed CDC indexing pipeline for campaign state.
- Delivered a rebuild/backfill system that indexed 14.3M campaigns, improving throughput 26× (150 → 3,900 campaigns/sec) and reducing rebuild time 95% (26 hrs → 1.5 hrs).
- Optimized Elasticsearch infrastructure footprint to reduce cloud spend by $60K/year, including 52.6% production and 83.2% staging infra cost reductions. Right-sized clusters based on cost/performance benchmarking while maintaining reliability targets.
- Kotlin
- Elasticsearch
- CockroachDB
- Kafka
- AWS
Goldman Sachs - Software Engineer Intern
New York, NY
- Developed a Kafka Message Correction Platform to address invalid messages, improving message accuracy and reducing rollbacks by 70%.
- Leveraged MongoDB Atlas as the platform backend.
- Implemented a comprehensive archival system for storing corrected events and metadata for future auditing.
- React
- Next.js
- Node.js
- Kafka
- MongoDB
- Spring Boot
Goldman Sachs - Software Engineer
Bengaluru, India
- Built user-facing dashboards to display investment banker deal information.
- Implemented a multi-project CI/CD strategy for multiple projects.
- Wrote applications to extract and massage data from data warehouses.
- React
- MongoDB
- Spring Boot
- ElasticSearch
- Spark
- Hadoop
- Kubernetes (K8s)
- Gitlab CI/CD
Amazon - Software Engineer Intern
Bengaluru, India
- Developed and deployed an AI-driven customer support chatbot.
- This reduced Amazon Prime customer query turnaround time by up to 90%. The system reduced support staff workload by 16%.
- AWS Lex
- Lambda
- DynamoDB
- AWS Amplify
- SQL
- Angular
- AWS Cloudformation
My projects
DoViz
Intervention-Centric Causal Inference Visualization
Jan 2024 — May 2024
Built DoViz, an interactive causal inference system that lets users simulate interventions, condition on confounders, and compare counterfactual scenarios with quantile-based visualizations. Built an end-to-end experimentation pipeline on AWS SageMaker for reproducible training, versioning, and deployment, and served low-latency real-time inference endpoints to power an interactive React client.
- React
- AWS SageMaker
- PyTorch
- Causal Inference
Generative Storywriter
LLMs & Illustration Synthesis
Aug 2023 — Dec 2023
Built a full-stack generative story application using GPT-3.5 for narrative generation and DALL-E for illustration synthesis. Implemented a backend pipeline that produces structured story outputs, splits them into page-level segments, launches parallel image generation jobs, and assembles the final storybook with response caching and structured logging across LLM API calls.
- OpenAI API
- DALL-E
- Node.js
KeyClass
Weak Supervision for Clinical Text
Jan 2023 — May 2023
Implemented the KeyClass multi-label architecture for weakly supervised clinical note classification using only class descriptions. Built an end-to-end NLP data pipeline on MIMIC-III (ingestion, preprocessing, and construction of multi-label training datasets) and benchmarked model reproducibility under constrained compute resources.
- PyTorch
- NLP
- MIMIC-III
Open Source Contributions
Contributed to AsyncAPI, OpenCollective, and Taiga UI. Multiple pull requests have been merged into the main branch. Wrote code to perform GitHub Actions integrations, End to End Testing, and enhancing front-end components.
- GitHub Actions
- TypeScript
- YAML
- Git
- Cypress
- Playwright
- Selenium
Learn 2 Earn: Certificate Gated Job Application System
Built a website where a user can only apply to certain jobs if they possess the certificates required by the jobs. The job applications on the website are token-gated by the certificates, allowing users to only apply if owners of them.
- React
- Next.js
- Solidity
- Foundry
- Hardhat
- EthersJS
- Metamask
Blockchain Based Raspberry Pi Mesh Network
Analyzed transaction and block propagation latencies within a private blockchain network of Raspberry Pis in a mesh configuration. Researched the correlation between these latencies and the number of Raspberry Pi nodes in the network.
- Solidity
- Docker
- Wireshark
- Raspberry Pi
Blockchain Based Course Feedback System
Designed a feedback system for University courses based on Solidity smart contracts deployed to Ethereum's test network. Assessed its ability to report immmutable, secure and bias-agnostic feedback by keeping feedback provider identities confidential.
- React
- Solidity
My skills
Languages
- Python
- Java
- JavaScript
- TypeScript
- C++
- C
- SQL
- Bash
- Rust
- Kotlin
Web & Frameworks
- HTML
- CSS
- React
- Next.js
- GraphQL
- Flask
- Django
- Node.js
Data & Databases
- Kafka
- MongoDB
- MongoDB Atlas
- CockroachDB
- ElasticSearch
- Snowflake
- DynamoDB
- Postgres
ML & AI
- AWS SageMaker
- PyTorch
- Causal Inference
- AI Agent Development
Cloud & DevOps
- AWS
- Azure
- GCP
- Lambda
- Lex
- Git
- Docker
- Jenkins
- Kubernetes (K8s)
- GitLab CI/CD
- CI/CD
- Linux
My Teaching Experience
Intro to Computer Science
CS 124: University of Illinois
Aug 2022 - May 2024
Teaching Assistant for CS 124: Introduction to Computer Science at the University of Illinois. Was a TA for four semesters.
Contact me
Please contact me at atharvchandratre.jobs@gmail.com or through this form.