Pulkit Agrawal

Lead ML Engineer

San Francisco, California, United States10 yrs 6 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in Machine Learning and Data Analysis.
Proven track record in developing deep learning models.
Experience in hardware design for low power systems.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in hardware design and computational algorithms.

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Skills

Core Skills

Machine LearningData AnalysisDeep LearningComputer VisionHardware DesignComputational Algorithms

Other Skills

OptimizationAlgorithmsData MiningLow Power SystemsNanoelectronicsPythonC++MatlabDigital Image ProcessingDigital Signal ProcessingComputer ArchitectureTeachingResearchVerilogVHDL

Experience

10 yrs 6 mos

Total Experience

3 yrs 6 mos

Average Tenure

9 yrs

Current Experience

Apple

Staff Machine Learning Engineer

Apr 2017 – Present · 9 yrs · Cupertino, CA · On-site

Foundation models training and inference optimization

Machine LearningData AnalysisOptimizationAlgorithms

Stanford university

Graduate Research Assistant

Apr 2016 – Dec 2016 · 8 mos · Stanford Artificial Intelligence Laboratory (SAIL)

I worked in Prof. Andrew Ng's Stanford ML group (https://stanfordmlgroup.github.io) on developing deep learning models for tracking and re-identifying people in retail-store videos to quantify customer engagement with products.

Deep LearningComputer VisionData Mining

Massachusetts institute of technology

Research Intern

May 2014 – Jul 2014 · 2 mos · Cambridge, MA

I worked in Prof. Anantha Chandrakasan's Energy Efficient Circuits and Systems group (https://eecsg.mit.edu) on designing a low power hardware accelerator for reconstructing light-fields from sparse samples.

Hardware DesignLow Power Systems

Indian institute of technology, bombay

Undergraduate Research Assistant

May 2012 – Mar 2013 · 10 mos · Mumbai Area, India

I worked in the Nanoelectronics Computation Lab with Prof. Swaroop Ganguly on developing computational algorithms for simulating cylindrical nanowire transistors.

Computational AlgorithmsNanoelectronics