Shivam Patel

AI Researcher

Mountain View, California, United States5 yrs 6 mos experience

Highly Stable

Key Highlights

Ranked 31 in the world on Project Euler
Research collaborations with top universities
Developed innovative machine learning applications

Stackforce AI infers this person is a Machine Learning expert with a strong focus on research and application in AI technologies.

Contact

patelshivam99@gmail.com LinkedIn

Skills

Core Skills

Machine LearningDeep LearningLarge Scale Data AnalysisResearch

Other Skills

Adobe PhotoshopAgent-based modelingAlgorithmsApplied Machine LearningBiological Data AnalysisCCSSCUDAClimate ChangeData AnalysisDebugging CodeDeep Reinforcement LearningEconomicsFlaskGame Theoretic Heuristics

About

I have been actively pursuing work in machine learning both in implementation and research domains. With a dozen of Machine Learning and Computer Vision based projects in my credit. Comfortable with Tensorflow, Theano, Torch, Caffe and the standard python libraries. Also worked in analytical number theory especially Ramanujan mathematics . Have been a keen programmer with interest in Algorithms, Data science,Machine Learning . Comfortable with C, C++, Java, Python,Perl, Matlab, Mathematica, MySQL, HTML, CSS, Javascript, MongoDB. Worked many open source projects like Golly, PowderToy etc. Ranked 31 in the world in Project Euler + at Hackerrank. Erdos number 2. I have had research collaborations from MIT, Harvard University, Stanford University and Caltech. I love to actively pursue theortic research in mathematical algorthims and applications of machine learning in logic.

Experience

Apple

Senior Machine Learning Engineer

Jun 2025 – Present · 9 mos · Cupertino, California, United States · Hybrid

Improving retrieval and ranking models for Retrieval-Augmented Generation (RAG) systems and agentic workflows for open-domain question answering powering state-of-the-art Large Language Models.

Large Language Models (LLM)Deep LearningSoftware DevelopmentMachine Learning

Google

Software Engineer - Machine Learning

Feb 2022 – Jun 2025 · 3 yrs 4 mos · Mountain View, California, United States · Hybrid

I worked on Applied Machine Learning predominantly on Google Ads. My work spans broad areas including building, improvement and optimization of Large Scale Recommendations System by using Nueral Architecture Search, Privacy Preserving Machine Learning Techniques and LLM based feature engineering.

Applied Machine LearningLarge Scale Recommendations SystemNeural Architecture SearchPrivacy Preserving Machine Learning TechniquesMachine LearningLarge Scale Data Analysis

Adobe

Machine Learning Research Intern

May 2021 – Aug 2021 · 3 mos · San Jose, California, United States

◦ Research: Worked with the Applied Science and ML Team for developing robust transformer-based multi-modal deep learning architectures for text-based video object segmentation and video retrieval. Deployed scalable systems to enable large scale text-based video editing pipelines. A patent is under filing for the work carried out.
◦ Development: Developed a modular, extensible and flexible framework for text-based video object segmentation. The framework was initiated with Python and CUDA implementation of Space-Time Correspondence Networkscoupled with Attention- Encoder-Decoder modules for multi-modal language and visual understanding. We deployed the application with Docker on an Amazon EC2 instance served with Flask.

Deep LearningPythonCUDAFlaskMachine Learning

University of cambridge

Visiting Research Student

Jan 2020 – May 2020 · 4 mos · Greater Cambridge Area

Working on my undergraduate thesis under the guidance of Dr Shahar Avin and Dr Jess Whittlestone at the Centre for the Study of Existential Risk. Here I developed a family of highly scalable and customizable agent-based models of AI research to understand the epistemology of machine learning research and how various factors like funding, research resources, hype and regulation impact it. These models were developed to have various deep reinforcement learning algorithms and game theoretic heuristics as a part of their decision making routines. Useful optimization techniques were developed to make them a useful computational tool for policy researchers, computer scientists, social scientists and philosophers.

Agent-based modelingDeep Reinforcement LearningGame Theoretic HeuristicsMachine LearningResearch

Mila - quebec artificial intelligence institute

Visiting Researcher

Jun 2019 – Jan 2020 · 7 mos · Montreal, Quebec, Canada

Worked under the Turing Laureate Prof Yoshua Bengio on areas at the intersection of Climate Change, Economics and Reinforcement Learning.

Reinforcement LearningClimate ChangeEconomicsMachine LearningResearch

Caltech

Visiting Undergraduate Researcher

May 2018 – Aug 2018 · 3 mos

Worked with Prof David Van Valen on using Deep Learning on segmentation of 3 Dimensional Cell segmentation of live real time biological data.

Deep LearningBiological Data AnalysisMachine LearningResearch

Massachusetts institute of technology

Invited Researcher

May 2017 – Jul 2017 · 2 mos · Boston, Massachusetts

I worked with Professor Gilbert Strang for mathematical formulation of Machine Learning and we explored various statistical methods and Tensor decomposition tools. He offered me to write a chapter about the same in his upcoming book.

Mathematical FormulationStatistical MethodsTensor DecompositionResearchMachine Learning