Hi, I am
Atharva Chandak.
I build intelligent systems.
I'm an ML Engineer currently building multimodal AI solutions at eBay. My journey spans research at Harvard, Mila (UdeM), CMU, and IISc - from continual learning and 3D segmentation to robotic vision - alongside industry experience in Deep Learning and scalable Full-Stack engineering.
Explore My Work
Experience
ML Engineer 2
eBay- Building Multi-Modal models to match user listings to catalog products for 1B+ items.
- Finetuning SOTA VLLMs for fine-grained image data extraction in Sports Trading Cards.
- Prototyped a buyer experience assistant for hyper-personalized recommendations with agentic memory.
Software Engineer
Wells Fargo- Full-stack developer for high-volume PL credit card/loan platforms processing 1M+ monthly transactions.
- Prototyped a scalable LLM chatbot for merchants, reducing application time by 30%.
- Built backend API microservices for POS EMIs and a SaaS customer insights portal.
Research Assistant
Mila / UdeM- Researched Continual Object Detection for autonomous driving, cutting catastrophic forgetting by 73%.
- Created on-device incremental OD methods for robotics using vision transformers.
Research Intern
Airlab, CMU- Engineered the vision pipeline for TartanAviation, generating 3.1M high-resolution images and 700k+ auto-labeled aircraft annotations.
- Designed optimized models for real-time aerial vehicle detection and tracking on drone hardware with multi-camera input.
- Analyzed OOD climatic conditions to improve model robustness across diverse weather scenarios.
Summer Intern
Wells Fargo- Built an end-to-end AI speech chatbot for home lending queries, integrating speech-to-text and TTS APIs for a full voice interaction loop.
- Implemented NLP pipeline for intent and entity extraction, achieving F1 scores of 93.4% and 83.5% respectively.
Visiting Research Intern
Visual Computing Group, Harvard- Contributed to PyTorch Connectomics, adding cellpose model support for neuron instance segmentation.
- Explored semi-supervised methods and designed an affinity learning + transformer pipeline to improve 3D segmentation accuracy.
Research Intern
AIRL, IISc- Worked on Generalized Continual Zero-Shot Learning, integrating incremental learning with ZSL for realistic DL deployment.
- Extended the setup to task-free, out-of-distribution generalization across diverse Computer Vision tasks.
Summer Intern
CSIR-CEERI Chennai- Applied traditional CV algorithms (FAST, ORB, BRISK) with ML classifiers (SVMs, KNNs) for texture classification of industrial images.
- Implemented segmentation networks to detect cracks and faults in leather, enabling automated quality inspection.
Education
BITS Pilani
Bachelor of Engineering in Computer Science
Coursework: Data Structures, Algorithms, Cloud Computing, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing.
Featured Work
AdQuestA: VQA for Advertisements
A multimodal VQA framework for ads achieving 58.58% accuracy on a new 94k+ QA pair benchmark. Integrates RAG with LLaVA for knowledge-grounded ad understanding. Published at WACV 2025.
LIFT-Net
A dual-pathway transformer attending to raw RGB and illumination-corrected frames for light-invariant action recognition. Achieved SOTA on HMDB51, UCF101, ARID, and InfAR.
Drone for Parcel Delivery
Optimized UAV navigation, control systems, and QR-based detection in a simulated environment, improving delivery success rates and accuracy. Top 5 in E-Yantra Competition.
Technical Arsenal
Languages
ML / AI
Frameworks
Cloud & Tools
Major Achievements
- Top 30 in India in IMC Prosperity 2, a global algorithmic trading competition by IMC Trading.
- 3rd Place in AI RoboSoccer Competition (148 teams) using PPO-based RL agents.
- Ranked 598 & 2449 out of 1.23M in JEE Main & Advanced.
- Top 5 in IIT Bombay's E-Yantra Robotics Competition.