MSCS (Honors) @ USC · ML / AI Engineer
Hi, I'm Aditya Jain.
I build machine learning that ships.
I'm a machine learning and software engineer on the Search team at Salesforce, where I build data and ML systems at scale — from Search Analytics, which streams tens of millions of rows per org from Apache Iceberg into customers' Data Cloud, to entity-prediction models on Salesforce's open-source ml4ir. I enjoy turning research ideas into production features people actually use.
I earned my MS in Computer Science with Honors (4.0 GPA) from USC, where I also TA'd Applied NLP (CSCI-544). Before that I spent two years as a Data Scientist at Cognizant working on search-ad click prediction and healthcare analytics. My interests span NLP, information retrieval, and computer vision — especially applications at the intersection of language and vision.
Writing
Blog
How GPT Works — Part 5: From Base Model to ChatGPT
The final step: how a raw next-token predictor becomes a helpful assistant. Part 5 covers pretraining, supervised fine-tuning, and RLHF, the difference between a base model and a chat model, plus the context window and KV-cache that govern inference.
How GPT Works — Part 4: Training & Generation
How a transformer learns and how it writes. Part 4 covers next-token cross-entropy training with an interactive loss-descent demo, then decoding strategies — greedy, temperature, top-k, and top-p sampling — you can reshape live.
How GPT Works — Part 3: The Transformer
How the attention mechanism becomes a working language model. Part 3 covers subword tokenization with live Byte-Pair Encoding, the transformer block (residuals, LayerNorm, MLP), the causal mask that makes a GPT decoder-only, and the full pipeline from text to next-token probabilities.
How GPT Works — Part 2: Attention
From the seq2seq bottleneck to the mechanism that replaced recurrence entirely. Part 2 builds attention from the ground up — soft alignment, scaled dot-product self-attention with Q/K/V, multi-head attention, and why a transformer needs positional encoding.
How GPT Works — Part 1: The Foundations
A visual, hands-on guide to how large language models work. Part 1 covers the only prerequisites you need — vectors, the dot product, matrix multiplication, and softmax — then the one idea the whole model is built on: next-token prediction.
Toolbox
Skills
Languages
- Python
- Scala
- Java
- C / C++
- SQL
- JavaScript
- HTML / CSS
ML / AI
- Machine Learning
- Deep Learning
- Reinforcement Learning
- Statistical Modelling
- Descriptive & Inferential Statistics
Frameworks & Libraries
- Keras / TensorFlow
- scikit-learn
- pandas
- matplotlib / seaborn
- NLTK
- pySpark
Tools & Platforms
- Git
- Docker / Swarm
- gRPC
- MongoDB
- Linux
- Web Development
- Android
Selected work
Projects
MLfromScratch
Classification, regression, and clustering algorithms — plus metrics and preprocessing helpers — implemented from scratch with NumPy for a deeper understanding of the math.
Brain Tumor Segmentation (MRI)
Implemented U-Net from "U-Net: Convolutional Networks for Biomedical Image Segmentation" to segment brain tumors in MRI scans.
FaceGAN — Generating Random Faces
Inspired by thispersondoesnotexist.com. Trained a Deep Convolutional GAN on 100k celebrity photos to generate photorealistic faces.
NER / POS Tagging App
An LSTM seq2seq model that tags words with their Named Entity or Part of Speech. Served with Flask and packaged with Docker.
Image Caption Generator
Implementation of the "merge" architecture from "What is the Role of RNNs in an Image Caption Generator?" using Keras.
Automatic Kinship Detection
A Kaggle challenge: given a pair of faces, determine whether they are related. Uses a Siamese network over VGG-Face.
Career
Experience
- May 2023 — Present
MTS Software Engineer
Salesforce, Inc.
San Francisco, California
- Jan 2023 — Apr 2023
Software Engineer
TaxBit, Inc.
Seattle, Washington
- Aug 2022 — Dec 2022
Teaching Assistant — Applied NLP
USC Viterbi School of Engineering
Los Angeles, California
- May 2022 — Aug 2022
Software Engineering Intern
Salesforce, Inc.
San Francisco, California
- Feb 2021 — May 2022
Student Research Assistant
USC Institute for Creative Technologies
Los Angeles, California
- Sep 2018 — Dec 2020
Associate Data Scientist
Cognizant Technology Solutions
Bengaluru, India
- Apr 2016 — Jul 2016
Intern — MEAN Stack Developer
Heelium Sports Pvt. Ltd.
Pune, India
Academics
Education
- Jan 2021 — Dec 2022
M.S. in Computer Science (Honors)
University of Southern California
Los Angeles, California
- Aug 2014 — Jun 2018
B.E. in Computer Science
Maharashtra Institute of Technology
Pune, India
Say hello
Get in touch
Have an opportunity, a question, or just want to talk ML? Drop a message and I'll get back to you.
- adityajn105@gmail.com
- Sunnyvale, CA