Rajat Jain

VP of Engineering

San Francisco, California, United States7 yrs 2 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Expert in Deep Learning and Machine Learning applications.
Proven track record in optimizing advertising algorithms.
Strong background in quantitative analysis and risk management.

Stackforce AI infers this person is a Machine Learning Engineer with a strong focus on AdTech and Fintech industries.

Contact

rajatja@google.com LinkedIn

Skills

Core Skills

Machine LearningDeep LearningData AnalysisCloud ComputingTeachingSoftware EngineeringQuantitative AnalysisRisk ManagementOptimizationAlgorithm Development

Other Skills

Large Scale ModelsRecommendation SystemsUser ExperienceBigQueryData MigrationFlaskGCPSQLAlchemyPostgreSQLHTMLCSSJavaScriptPythonJavaData Structures

About

Interested in the application of Deep Learning & LLMs to solve real-world problems.

Experience

7 yrs 2 mos

Total Experience

3 yrs 7 mos

Average Tenure

6 yrs 6 mos

Current Experience

Google

2 roles

Tech Lead

Feb 2020 – Present · 6 yrs 3 mos · Mountain View, California, United States · On-site

As part of the Youtube Ads Machine Learning team, I work on building and deploying SotA large scale Deep Learning prediction and recommendation models. My objective is to utilize research & advancements in LLMs and Deep Learning to 1) enhance user experience, 2) provide optimal return on investment to Advertisers, and 3) maximize sustainable revenue for Google.
Previously: designed, built and deployed Machine Learning models to identify and recommend high quality Apps in the Google Play Store.

Deep LearningMachine LearningLarge Scale ModelsRecommendation SystemsUser Experience

Software Engineer Intern

May 2019 – Aug 2019 · 3 mos · Greater New York City Area

Decommissioned 2 legacy data sources and moved data to a highly scalable & cloud-based data warehouse, BigQuery, which reduced processing time significantly and saved around $0.5M / quarter.
Worked on the project end to end, right from the Product Requirement Document(PRD) stage to final pipeline implementation, testing and verification stage.

BigQueryData MigrationCloud ComputingData Analysis

The university of texas at austin

2 roles

Teaching Assistant, Software Engineering

Jan 2019 – May 2019 · 4 mos · Austin, Texas Area

Worked as a Teaching Assistant for the 'Elements of Software Engineering' undergraduate course.
Conducted weekly lab sessions for the course, graded project assignments, took topic lectures.
Assignments were related to Flask, GCP, SQLAlchemy, PostgreSQL DB, HTML, CSS, JavaScript, Git, and Python.

FlaskGCPSQLAlchemyPostgreSQLHTMLCSS+4

Teaching Assistant, Data Structures

Aug 2018 – Dec 2018 · 4 mos · Austin, Texas Area

Worked as a Teaching Assistant for the 'Data Structures: Honors' undergraduate course.
Took lectures on debugging, exception handling, coding practices, shell commands, testing(black, white, unit, Junits), Object-oriented programming, Data Structures( like AVL Trees, BST, Stacks, Queues, Heaps etc.) and complexity analysis.
Conducted discussion sessions, graded programming assignments related to data structures and programming in Java.

JavaData StructuresObject-Oriented ProgrammingTeachingSoftware Engineering

Goldman sachs

2 roles

Quantitative Analyst II

Jun 2016 – Jul 2018 · 2 yrs 1 mo · Bengaluru Area, India

Worked on modeling and projecting the liquidity risk of the firm using the knowledge of Machine learning, Algorithms, and Mathematics.
Led the Asia initiatives of Corporate Treasury Strats team.
Built a tool to strategically optimize and manage the High-quality Liquid Assets(HQLA).
Worked on building algorithms and techniques for attribution of liquidity risk to business units.
Mentored intern project on the day over day projections of liquidity using statistical modeling.
Worked on the testing framework for booking callable loans in different currencies.

Machine LearningAlgorithmsStatistical ModelingQuantitative AnalysisRisk Management

Summer Intern

May 2015 – Jul 2015 · 2 mos · Bengaluru Area, India

Developed a lite incremental version of the collateral allocation optimizer using Linear Programming, Graphs, Complexity Theory, & Approximations.
The lite version runs 50 times faster than original optimizer which helped the business teams in running scenario analysis much more efficiently.

Linear ProgrammingGraphsComplexity TheoryOptimizationAlgorithm Development