Rishabh Yadav

Software Engineer

San Francisco, California, United States10 yrs 10 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in monetization strategies for VR applications.
Proven track record in optimizing software performance.
Strong foundation in machine learning for software defect prediction.

Stackforce AI infers this person is a Software Engineer specializing in SaaS and Machine Learning applications.

Contact

Skills

Core Skills

Software EngineeringMonetizationUi DesignMachine Learning

Other Skills

App StoreVR AppsTech LeadingBranded Content AdsTippingSubscriptionsPaid Online EventsGroupsNFTsMulti-State ObjectsAgile SDLCPrototypingAgile DevelopmentBug PredictionLogistic Regression

About

I love Software Engineering and am lucky to pursue that as a profession. I like solving real-life problems. I believe in simple solutions which scale well and can be extended. I like designing ecosystem where different pieces of code work together, which can be extended. My most treasured values are work ethic and equanimity. As a developer, I believe in the 'Domino effect', believe in software development sustainability. As a result, I have a keen interest in building for the long term, working with like people, and peer learnings. In my time away from Coding I enjoy photography. My alternate would be a Maths Teacher or a Cityscape Photographer.

Experience

10 yrs 10 mos

Total Experience

3 yrs 7 mos

Average Tenure

8 yrs 7 mos

Current Experience

Facebook

2 roles

Software Engineer

Feb 2018 – Present · 8 yrs 4 mos · Menlo Park, California

Monetization & App Store for Meta VR Apps. Working on the Monetization & App Store for Meta VR Apps.
Working on VR app store and monetization
Creator Monetization: Helping Facebook Creators moentize their content. Wokring on multiple 0-1 products, Tech-Leading multiple workstreams
Branded Content Ads - www.facebook.com/formedia/tools/branded-content
Tipping (Stars)- www.facebook.com/creators/tools/stars
Subscriptions - www.theverge.com/2018/3/19/17138888/facebook-creators-tools-test-sponsor-badge-fans
Paid Online Events - techcrunch.com/2020/08/14/facebook-paid-online-events/
Groups - techcrunch.com/2018/06/20/facebook-subscription-groups/
NFTs

MonetizationApp StoreVR AppsTech LeadingSoftware Engineering

Software Engineer Internship

May 2017 – Aug 2017 · 3 mos · Menlo Park

Texas a&m university

Student Assistant

Jan 2017 – May 2017 · 4 mos · College Station, Texas

Grader for Analysis of Algorithms.

Adobe

2 roles

Senior Member of Technical Staff

Jan 2016 – Aug 2016 · 7 mos · Bangalore

Developing the flagship feature, ‘Multi-State Objects’, of Adobe Captivate 9 improving the authoring time by 20%.
Hands on experience with Agile SDLC.
Optimizing the real-time performance of the Adobe captivate by 30% for faster and smoother user experience.
Prototyping and development of notifications feature for the online user community of Adobe Captivate.
Responsible for prototyping of new feature, ‘Device capture’.
Responsible for the UI design and implementation of Adobe captivate.

Multi-State ObjectsAgile SDLCUI DesignSoftware Engineering

Member of Technical Staff

Sep 2014 – Aug 2016 · 1 yr 11 mos · Bangalore

Developing the flagship feature, ‘Multi State Objects’, of Adobe Captivate 9 improving the authoring time by 20%.
Hands on experience with Agile development cycle.
Optimizing the real-time performance of the Adobe captivate by 30% for faster and smoother user experience.
Prototyping and development of notifications feature for the online user community of Adobe Captivate.
Responsible for prototyping of new feature, ‘Device capture’.
Responsible for the UI design and implementation of Adobe captivate.

Multi-State ObjectsAgile DevelopmentUI DesignSoftware Engineering

Infosys

Machine Learning Intern

May 2013 – Jul 2013 · 2 mos · Bengaluru Area, India

I worked on Analysis and Evaluation of Performance of Network Metrics for Bug Prediction in Software’. Code-based metrics and network analysis based metrics, which are widely used to predict defects in software, were used in this project. Our goal was to evaluate the performance of these metrics individually and in combination, using three different machine learning techniques - Logistic Regression, Support Vector Machines (SVM) and Random Forests. My role was to develop the model based on a dataset of 11 open source projects from the PROMISE repository. For each project we built binary classification models to identify bugged files. Our work was published at the 20th Asia Pacific Software engineering conference and I was acknowledged for my contribution to it.

Machine LearningBug PredictionLogistic RegressionSVMRandom ForestsSoftware Engineering