Utkarsh Patel

Software Engineer

Seattle, Washington, United States5 yrs 11 mos experience

Highly Stable

Key Highlights

Expert in machine learning and computer vision.
Proven experience in systems design and back-end development.
Strong academic background with a 3.95 GPA in Computer Science.

Stackforce AI infers this person is a Software Engineer with expertise in machine learning and systems design.

Contact

utkarshpatel@amazon.com LinkedIn

Skills

Core Skills

Systems DesignBack-end Web DevelopmentMachine LearningComputer VisionSoftware Infrastructure

Other Skills

Accounting and FinanceAgile MethodologiesAlgorithmsAttention-based AutoencoderCC++Cloud ComputingCompilersComputer NetworksCustomer Relationship Management (CRM)Data StructuresDatabase DesignDatabase SystemsDeep LearningEngineering

Experience

5 yrs 11 mos

Total Experience

3 yrs 7 mos

Average Tenure

2 yrs 4 mos

Current Experience

Amazon

Software Engineer

Feb 2024 – Present · 2 yrs 4 mos · Seattle, Washington, United States · On-site

Salesforce

2 roles

Member Of Technical Staff

Promoted

May 2022 – Feb 2024 · 1 yr 9 mos · San Francisco Bay Area

Systems DesignBack-End Web Development

Associate Member Of Technical Staff

Jul 2020 – May 2022 · 1 yr 10 mos · San Francisco Bay Area

Adobe

Research Intern

May 2019 – Jul 2019 · 2 mos · Noida

▪ Developed a system for modeling fashion compatibility of items in an outfit using only visual cues.
▪ Graph Neural Networks based model for compatibility prediction and Attention-based Autoencoder model
for clustering outfits based on their styles were developed as a part of the project.
▪ Stress-tested the current evaluation metric and found a fundamental discrepancy. Suggested a new metric that is
more scalable in real-world scenarios. The new model beats the current state-of-the-art model significantly.
▪ Research paper accepted in top-tier conference WACV'20.

Maximl

Summer Internship

May 2018 – Jul 2018 · 2 mos · Chennai, Tamil Nadu, India

Designed and coded a regression testing framework for testing the numerical outputs of a graph-theoretic algorithm library.
Developed visual language for non-technical user to create/edit numerical regresssion tests through intuitive user interface.
Architected a real-time, remote test platform that can run tests on any internet-accessible server which has the library installed.