Adithya Samavedhi

Adithya Samavedhi

Software Engineer II @ The Trade Desk

The Trade Desk

Biography

I’m a Software Engineer II at The Trade Desk in Seattle, where I work on the data platform that ingests and propagates billions of user ID–segment pairs across datacenters. Most of my work lives at the intersection of distributed systems and data infrastructure: Kafka-based propagation pipelines, Snowflake and Databricks integrations, rate limiters, and the open-source SDKs (ttd-data, ttd-databricks) that clients use to talk to our Data APIs.

Before this I was at Goldman Sachs on the Middleware Engineering team, and I hold an M.S. in Computer Science from UC San Diego (GPA 4.0) and a B.E. from BITS Pilani, Goa. Along the way I’ve published research on long-document comparison, fake news profiling, and adversarially robust malware detection.

Download my resumé.

Interests
  • Distributed Systems
  • Data Infrastructure & Streaming
  • Machine Learning
  • Natural Language Processing
Education

  • M.S. Computer Science — GPA 4.0/4.0, 2024

    University of California, San Diego

  • B.E. Computer Science — GPA 8.71/10.0, 2021

    Birla Institute of Technology & Science, Goa Campus

Skills

Languages

Python, C#, SQL, C++, Go, Java, C, Shell

Infrastructure

Kubernetes, Docker, AWS, Kafka, Redis, RabbitMQ, Databricks

Data & Processing

Apache Spark (PySpark), Delta Lake, Databricks, Snowflake, Kafka

Databases

PostgreSQL, MongoDB, Snowflake, Aerospike

Frameworks

.NET 8, FastAPI, Django, SQLAlchemy, MongoEngine, Celery

APIs

REST, GraphQL, gRPC, OpenAPI

Observability

Prometheus, Grafana, Honeycomb

Tools

Git, Linux

Experience

 
 
 
 
 
The Trade Desk
Software Engineer II
The Trade Desk
Oct 2025 – Present Seattle, USA
  • Authored and open-sourced ttd-data, a PyPI SDK for The Trade Desk’s Data APIs, adopted by clients driving 5,000+ requests/second of production traffic; standardized authentication, retries and rate-limit handling across integrations
  • Built and open-sourced ttd-databricks, a PySpark SDK enabling clients to push segment data from Databricks to TTD’s Data APIs, supporting incremental Delta Lake batch pipelines with checkpointing and per-row error tracking
  • Engineered a tree-based boolean attribution engine to process pixel fires across client websites, directly supporting $27M in attributed platform spend
 
 
 
 
 
The Trade Desk
Software Engineer I
The Trade Desk
Apr 2024 – Oct 2025 Seattle, USA
  • Built a Snowflake Native App ingesting 10B+ user id–segment pairs, enabling fully self-serve onboarding for 24 data providers
  • Architected a Kafka-based data propagation system, cutting end-to-end processing time 90% and infrastructure costs $300K/year
  • Designed and deployed a Redis-based distributed rate limiter to protect data ingestion services from exploitation, projected to save 150+ engineering hours per year
 
 
 
 
 
The Trade Desk
Software Engineer Intern
The Trade Desk
Jun 2023 – Sep 2023 Irvine, USA
  • Eliminated redundant cross-datacenter propagation by tracking user-location affinity and routing segment data only to relevant datacenters, cutting propagation traffic and network costs by 60%
 
 
 
 
 
Goldman Sachs
Software Engineer
Goldman Sachs
Jul 2021 – Jul 2022 Bengaluru, India
  • Engineered a Celery-based scheduling service exposing APIs to create and manage Kubernetes CronJobs, autonomously orchestrating database operations across 4,000+ middleware application endpoints
  • Developed GSMDWUtility, a Python library providing reusable REST middlewares (OIDC JWT validation, APM labeling, metadata augmentation) to standardize microservices across departments
  • Mentored an intern in building a monitoring dashboard for bare-metal OS patching, tracking canary-to-fleet rollout status, OS version distribution and patch success across on-prem servers
  • Technologies used: Python, Java, Kubernetes, Prometheus, Celery
 
 
 
 
 
Couture AI
Machine Learning Intern
Couture AI
Jan 2021 – Jun 2021 Hyderabad, India
  • Built a video recommendation system leveraging LightGBM and collaborative filtering
  • Designed a candidate generation pipeline spanning 20M videos and 6M users
 
 
 
 
 
Virginia Polytechnic Institute and State University
Research Intern
Virginia Polytechnic Institute and State University
Nov 2020 – Dec 2020 Remote
  • Implemented four baseline neural network models for long document comparison: HAN, SMITH, DSSM and ARC-1
  • Created two benchmark datasets for long document comparison from the Wikipedia repository and ACL AAN
  • Co-authored a long research paper
 
 
 
 
 
Telangana e-Governance
Software Developer Intern
Telangana e-Governance
May 2019 – Jul 2019 Hyderabad, India
  • Designed a static website to showcase the projects of Telangana Life Sciences corporation
  • Worked along a team of 5 to survey, design and implement the website
  • Technologies used: HTML, CSS, Javascript
 
 
 
 
 
Goldman Sachs
Software Engineer Intern
Goldman Sachs
May 2020 – Jun 2020 Bengaluru, India
  • Developed a PostgreSQL audit agent that parsed database logs across 800+ servers, classifying connection events, SQL queries and errors into structured audit streams for centralized collection

Projects

*
All Distributed Systems Open Source Deep Learning
ttd-databricks — PySpark SDK for Databricks → TTD Data APIs
  • Open-source PySpark SDK that lets clients push segment data from Databricks to The Trade Desk’s Data APIs
  • Incremental Delta Lake batch pipelines with checkpointing, per-row error tracking and distributed HTTP connection reuse across Spark workers
ttd-data — Python SDK for The Trade Desk Data APIs
  • Open-source PyPI SDK for The Trade Desk’s Data APIs, adopted by clients driving 5,000+ requests/second of production traffic
  • Standardized authentication, retries and rate-limit handling across client integrations
Surfstore — A Fault-Tolerant Dropbox Clone
  • Built a fault-tolerant distributed file store scaling to 128 nodes with concurrent shared-file access and full CRUD support
  • Used RAFT-replicated MetadataStore and BlockStore services to maintain availability with up to 50% node failures
Networked Sort — Distributed Sorting Program
  • Developed a distributed sort over raw socket programming spanning 16 server nodes
  • Built an even data-partitioning algorithm that sorted 2GB+ datasets in under 15 seconds
Designing Protective Measures against Malware Attacks
  • Devised a gradient based, greedy attack strategies to modify malicious applications to fool detection systems
  • Designed a novel defensive strategy to protect detection systems from malware attacks
  • Validated the strategies for twelve classification algorithms against 50,000 android applications
Designing Protective Measures against Malware Attacks
Detection of Malaria from Segmented Human Cell Images
  • Built detection models using convolutional neural networks in Pytorch implementing a custom architecture and transfer learning from state-of-art models
Empirical Analysis of Student Feedback (Confidential Project)
  • Pre-processing raw feedback data collected across 10 streams, 200+ courses, 100+ faculty
  • Implementing sentiment analysis models, clustering objective responses, deriving inferences through knowledge graphs
Profiling Fake News: Learning the Semantics and Characterisation of Misinformation
  • Proposed Fake News detection model using extracted syntactic, semantic, temporal features
  • Evaluated the hypothesis against four benchmark datasets of varying article lengths
Supervised Contrastive Learning for Interpretable Long Document Comparison

Recent Publications

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Swati Agarwal, Adithya Samavedhi (2022). Profiling Fake News: Learning the Semantics and Characterisation of Misinformation. ADMA'22.

Akshita Jha, Adithya Samavedhi, Vineeth Rakesh, Jaideep Chandrashekar and Chandan K Reddy (2022). Transformer-based Models for Long Document Comparison: Challenges and Empirical Analysis.

Hemant Rathore, Adithya Samavedhi (2021). Are Malware Detection Models Adversarial Robust Against Evasion Attack?. INFOCOM'22.

Akshita Jha, Jaideep Chandrashekar, Vineeth Rakesh, Adithya Samavedhi and Chandan K Reddy (2021). Supervised Contrastive Learning for Interpretable LongDocument Comparison.

Hemant Rathore, Adithya Samavedhi, Sanjay K. Sahay and Mohit Sewak (2021). Robust Malware Detection Models: Learning from Adversarial Attacks and Defenses.

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