Research Intern, NVIDIA Metropolis
Jun '26 — Present
Research Assistant
Sept '24 — Present
ML Research Engineer — L3 (Specialist)
Jul '24 — Aug '24
- Worked on further improving the perishable prediction algorithm by incorporating probability distributions and uncertainty estimation.
- Laid the groundwork for personalized recommendations and marketing for the Chaldal platform.
- Created Superset dashboards to monitor real-time performance of the ML models deployed in production, enabling quick identification and resolution of issues.
- Ported the existing ML systems to our new Kubernetes based infrastructure.
- Onboarded new team members and helped them get up to speed with the projects.
ML Research Engineer — L3
Feb '24 — Jun '24
- Got promoted to Level 3 in record time of the company’s history!
- Developed a new Machine Learning driven perishable demand prediction algorithm that is a 70% improvement from its predecessor and is estimated to save 2000K BDT per month.
- Integrated the new address search feature into the core Chaldal platform (website and app). Gives users a more accurate and faster address search experience in the checkout process.
- Worked on creating a geospatial language model to help optimize delivery agents to deliver orders faster and more efficiently.
ML Research Engineer — L2
Jun '23 — Jan '24
- Led the research and development of a new address search feature. Improved the existing Lucene based search using fine-tuned LLM, retrieval augmented generation (RAG), and re-ranking. Improved the accuracy to over 60% from less than 10% after taking over the project. The project greatly reduced the need for humans in the loop for this essential process and has optimized delivery logistics.
- Developed a reinforcement learning based in-house recommender system from the ground up for category based product recommendation.
- Created an AB testing framework for the recommender system that handles quick experiment iterations with no downtime! It is used to evaluate the performance of different recommendation models in production, currently tested on 200K+ customers.
Research Intern
Oct '22 — Apr '24
- Remotely worked as a Research Intern at Dr. Min Xu’s lab at Carnegie Mellon University.
- Worked on developing an annotation-efficient semi-supervised particle-picking framework for 3D object detection from macromolecular samples. The goal was to detect and classify particles from cryo-electron tomograms having signal-to-noise ratio as low as 0.1, making it a very challenging problem. (Manuscript in preparation)
IICT, BUET
Backend Engineer
Dec '21 — Feb '23
- Developed a comprehensive online portal for advance payment application and tracking for the Directorate of Advisory, Extension and Research Services (DAERS), BUET.
- Designed and implemented the backend from scratch to production, using Django Rest Framework, PostgreSQL, and Docker, according to the client’s custom needs and workflow.
- The system was launched in February 2023 for internal use at BUET.
Big Data Intelligence Lab, Auburn University
Research Intern
May '21 — Mar '22
- Worked in the Big Data Intelligence (BDI) Lab under Dr. Shubhra Kanti Karmaker, Assistant Professor, Auburn University, Alabama, on Short Text Stream Clustering.
- Developed with a team a new software tool called One Pass Sentence Embedding Clustering (OPSEC), designed to efficiently cluster short text streams.
- Utilized a unique one-pass algorithm that calculates similarity scores based on sentence embeddings to cluster the short texts.
- Outperforms current state-of-the-art methods in terms of both accuracy and quality of clustering for short text streams.
Unilever
Machine Learning Intern, Unilever Forecast Engine
Nov '21 — Dec '21
- Worked with a team that developed a new sales forecasting model for Unilever Bangladesh.
- Applied data engineering techniques on raw sales data.