Arnav Garg

Machine Learning Lead, Senior Machine Learning Engineer

2024 - Current

Leading Predibase’s machine learning team. Recent work includes leading the development of our reinforcement fine-tuning offering, co-creating Turbo LoRA for efficient fine-tuning + 3x faster inference via speculative decoding, developing a synthetic data generation algorithm that beats K-shot GPT-4o with just 10 rows, building continuous LoRA training mechanisms, and co-authoring LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4.

Senior Machine Learning Engineer

2023 - 2024

I focus on applied machine learning, optimizing fine-tuning workflows, and scaling distributed training and inference for open-source LLMs. My work includes designing reliability mechanisms to make training cost-effective, efficient, and resilient—so users can focus on iteration, not infrastructure. I'm also a lead maintainer of Ludwig, an open-source, YAML-based framework for low-code multimodal deep learning. 🔗 Explore my open-source work: github.com/arnavgarg1

Machine Learning Engineer

2022 - 2023

Machine Learning Scientist, Core Machine Learning

2022 - 2022

Building machine learning powered smart features for Confluence and Trello. Some of the things I was responsible for during my time at Atlassian: 1. Building models to suggest users and spaces to follow on the Confluence Home Feed 2. Content recommendations across Confluence, including general suggestions and related pages (patented) 3. Suggesting users to invite to boards and workspaces on Trello 4. Propensity modeling for Confluence editions 5. Built internal tooling to quickly test models without full frontend or backend integration.

Associate Machine Learning Scientist, Core Machine Learning

2021 - 2022

Mentor

2021 - 2021

Technical Advisor

2021 - 2021

Co-Founder and President

2018 - 2020

I co-founded DataRes, UCLA’s first data science and machine learning organization that caters to everyone from undergraduates to PhDs. Website: https://ucladatares.com/ Facebook: https://www.facebook.com/ucladatares/ Medium: https://medium.com/@ucladatares

Machine Learning Scientist Intern, Core Machine Learning

2020 - 2020

I was part of Atlassian's Core Machine Learning (CML) team, the centralized ML group, and their first intern hire in the US. I worked on scaling feature generation across product-focused machine learning using self-supervised learning, using ideas inspired by SOTA NLP.

Product Manager at OpenAQ

2020 - 2020

I led 4 developers to work on open-source air quality data aggregation services for NASA Global Modeling and Assimilation Office (GMAO) and the World Resources Institute (WRI).

Fellow

2020 - 2020

I was a part of a group of 24 fellows across 5 countries (< 1% acceptance rate).

Software Engineering Intern

2019 - 2019

As part of Tesla's Low Voltage Controllers (Electronic Systems) team, I identified and resolved a critical flaw in the Autopilot SOC validation manufacturing process, significantly enhancing the robustness testing of Autopilot hardware (HW 2.5 and HW 3.0). I also developed a real-time dashboard to monitor and detect issues in Autopilot SOC stress test systems across Tesla’s global manufacturing network.

Software Engineering Intern, Machine Learning

2019 - 2019

As part of Expressive's backend and machine learning teams, I developed and productionized deep learning models (BERT, Transformers, CNNs) for tasks like sentence similarity, metaphor paraphrasing, information retrieval, and context comprehension, improving the accuracy of Expressive's virtual service agent. On the backend, I implemented a feature to bulk import knowledgebase articles from ServiceNow, significantly reducing onboarding time.

Software Engineer

2018 - 2019

As an early employee at Kona (formerly Sike Insights), a Kleiner Perkins-backed startup, I helped build a web application (now a Slack extension) for remote teams to assess EQ compatibility and provide managers with personalized insights to enhance productivity and reduce turnover. I also developed an encryption layer around DynamoDB to ensure secure handling of user data.

Data Science Intern

2018 - 2018

I was part of a global team of 25 data scientists across the company. I developed a deep neural network to predict the likelihood of users installing the Starbucks mobile app if shown Starbucks advertisements that significantly improved the Starbucks app installation conversion rate.