Ritvik Kapila

Co-Founder

San Francisco, California, United States5 yrs 10 mos experience

Most Likely To Switch

Key Highlights

Published research on zero-shot learning at AAAI 2025.
Contributed to AWS encryption libraries and SDKs.
Led workshops on Quantitative Trading and Machine Learning.

Stackforce AI infers this person is a Machine Learning and Software Development specialist in the SaaS industry.

Contact

Skills

Core Skills

Software DevelopmentMachine Learning

Other Skills

encryptionRusttime-series modelingmulti-modal learningdomain generalizationobject detection

About

ex-data research @ Essential AI for pre-training LLMs. Previously, I did my Masters in Computer Science at UC San Diego, where I worked as a Graduate Research Assistant, with a strong background in Machine Learning, Deep Neural Networks, Natural language processing, and time-series modeling. I worked with Prof. Rajesh Gupta and Prof. Jingbo Shang on zero-shot learning for Human Activity Recognition (HAR) time-series data. My masters research was published at AAAI 2025: "ZeroHAR: Sensor Context Augments Zero-Shot Wearable Action Recognition". I completed my B.Tech degree in Electrical Engineering at IIT Delhi in 2021, where I received multiple honors and awards for academic excellence and research. I also attended a Student Exchange Program at INSA Lyon, France, in 2019, where I enhanced my skills in Computer Science and French. I am passionate about applying my skills and knowledge to solve real-world problems and creating a positive impact. More information about my research interests can be found on my website and google scholar. Website: https://ritvikkapila.github.io/ Google Scholar: https://scholar.google.com/citations?user=vTYNQkwAAAAJ For any information please feel free to drop me an email at: ritvik.iitd@gmail.com

Experience

5 yrs 10 mos

Total Experience

1 yr 2 mos

Average Tenure

1 yr 5 mos

Current Experience

Startup communities

Member

Jan 2025 – Present · 1 yr 5 mos · San Francisco, California, United States · On-site

part of startup communities at OpenAI, SPC, Foundation Capital (IIT Build), and a few others!
If you are a builder early in your journey, would love to support you. Please feel free to reach out!

Essential ai

Machine Learning Researcher

Jan 2025 – Jan 2026 · 1 yr · San Francisco, CA, US · On-site

Data; Build frontier LLMs

Amazon

Software Engineer

Apr 2024 – Mar 2025 · 11 mos · United States · On-site

Actively contributed to the following encryption libraries:
https://github.com/aws/aws-database-encryption-sdk-dynamodb
https://github.com/aws/aws-encryption-sdk
https://github.com/smithy-lang/smithy-dafny
Released the Encryption SDK in Rust:
https://github.com/aws/aws-encryption-sdk/tree/mainline/AwsEncryptionSDK/runtimes/rust
https://crates.io/crates/aws-esdk

encryptionsoftware developmentRust

Triton quantitative trading

Founding Board Member

Sep 2023 – Apr 2024 · 7 mos · San Diego, California, United States · On-site

Held workshops and trainings for undergraduate and graduate students about Quantitative Trading and Machine Learning.

Uc san diego

2 roles

Graduate Research Assistant

Apr 2023 – Apr 2024 · 1 yr · San Diego, California, United States · On-site

Working as a Graduate Research Assistant with Prof. Rajesh Gupta and Prof. Jingbo Shang on multi-modal machine learning for Human Activity Recognition (HAR) to align time-series data with other modalities.

Graduate Research Assistant

Jun 2022 – Mar 2023 · 9 mos · San Diego, California, United States · On-site

Working as a Graduate Researcher at the Adaptive Computing and Embedded Systems lab at UCSD with Prof. Farinaz Koushanfar.

machine learningtime-series modelingmulti-modal learning

Mit media lab

Machine Learning Researcher

Dec 2022 – Mar 2023 · 3 mos · Cambridge, Massachusetts, United States · Hybrid

Research paper @ ICLR 2023 workshop titled “Domain Generalization in Robust Invariant Representation” (https://arxiv.org/abs/2304.03431)
Description: Investigated generalization of invariant transformations on out-of-distribution data for object detection