Mohit Agarwal

AI Researcher

San Francisco, California, United States11 yrs 6 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in Machine Learning and AI research.
Proven experience in developing LLMs and health AI solutions.
Strong background in wireless communications and signal processing.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in AI Research and Telecommunications.

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Skills

Core Skills

Machine LearningArtificial IntelligenceWireless Communications SystemsDeep Learning

Other Skills

Large Language Models (LLM)Signal ProcessingResearchC++TensorflowBayesian ModelDigital ModulationMatlabCProgrammingJavaBrain-Computer InterfacesEEGTime-Series AnalysisComputer Networking

Experience

Google

Senior Machine Learning Engineer

Feb 2023 – Present · 3 yrs 1 mo · Mountain View, California, United States · On-site

Dec 2024 - Current: Post-training interventions (SFT/RL) for production-level LLMs (AI Mode and Gemini revisions)
Feb 2023 - Nov 2024: Google Research Health AI Team on Environmental and Geospatial/Population Health

Large Language Models (LLM)Machine LearningArtificial IntelligenceSignal Processing

Goldman sachs

2 roles

Quantitative Strategist, Vice President

Promoted

Jan 2023 – Feb 2023 · 1 mo · New York, United States · On-site

Quantitative Strategist, Associate

Feb 2021 – Dec 2022 · 1 yr 10 mos · New York, United States · On-site

Apple

Core Wireless Internship

May 2018 – Aug 2018 · 3 mos · Cupertino, CA

Summer Internship in Core Wireless Team:
Developed a system-level discrete event simulator in C++ to characterize and optimize the parameters of a radio-access technology (undisclosed, and developed in-house)

C++Wireless Communications Systems

Lawrence livermore national laboratory

Machine Learning Internship

May 2017 – Aug 2017 · 3 mos · United States

High-Dimensional Spectral Sampling Methods - A Machine Learning Approach:
Automated the Pair-Correlation Function (PCF) estimation for arbitrary point clouds (which traditionally either require manual tuning for estimation, or takes several days for MD simulation)

Machine Learning

Cisco

Deep Learning Internship

May 2016 – Aug 2016 · 3 mos · San Francisco Bay Area

We built a deep neural network that is able to recognize different activities or actions from video clips. Written in tensorflow, we deploy a stateful architecture which leverages the temporal coherence in the sequences of video frames. The fluid architecture of the model allows it to classify actions on a video livefeed. We experimented with various architectures on UCF-101 dataset, to quantify the comparison between stateful and stateless models in case of videos.

Deep LearningTensorflow

Georgia institute of technology

Graduate Research Assistant

Aug 2014 – Feb 2021 · 6 yrs 6 mos · Atlanta, Georgia

Human-Assisted Machine Learning
(+) Worked at the intersection of reinforcement learning and human-computer interfaces.
(+) Application of Machine Learning (ML) and standard Signal Processing (SP) algorithms in neural data recorded from the human brain

Machine LearningSignal Processing

Syracuse university

Student Summer Intern

May 2013 – Jul 2013 · 2 mos · Syracuse, New York Area

Summer Research Internship under Prof. Pramod K. Varshney in Telecommunications
(+) Developed algorithm for automatic identification of digital modulation in wireless communication in the presence of noisy environment having unknown channel parameters using Bayesian model
(+) ProposedCollapsed Gibbs sampling-based approach for channel parameter estimation
(+) Performs with more than 90% correct classification probability for higher-order QAMs

Bayesian ModelDigital ModulationWireless Communications Systems