Sanyam M.

AI Researcher

New York, New York, United States10 yrs 4 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Experienced in Machine Learning and Deep Learning technologies.
Strong background in software development and project management.
Hands-on experience with computer vision applications.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in software development and project management.

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Skills

Other Skills

Product ManagementProduct DevelopmentSoftware DevelopmentSoftware Project ManagementDeep LearningComputer VisionMatlabJavaHadoopVHDLSynopsys Primetime

Experience

10 yrs 4 mos

Total Experience

2 yrs 1 mo

Average Tenure

2 yrs 3 mos

Current Experience

Instagram

Tech Lead, Machine Learning

Jan 2024 – Present · 2 yrs 3 mos · New York, United States · On-site

Stanford university

4 roles

Research Assistant - Stanford InfoLab, Dr. Chris Ré

Mar 2018 – May 2018 · 2 mos

Course Assistant - Mining Massive Datasets (CS246)

Jan 2018 – Mar 2018 · 2 mos

Course Assistant - Machine Learning (CS229)

Sep 2017 – Dec 2017 · 3 mos

Graduate Student Researcher - Stanford Vision Lab, Dr. Fei Fei Li

Mar 2017 – Feb 2018 · 11 mos

Focal systems, inc.

Deep Learning Engineer

Jan 2018 – Jan 2022 · 4 yrs · San Francisco Bay Area

Schlumberger

Machine Learning Intern - Computer Vision

Jan 2017 – Jan 2017 · 0 mo · Palo Alto

The boston consulting group

Associate - Management Consulting

Jan 2014 – Jan 2016 · 2 yrs · Mumbai, Maharashtra, India

I have worked across five different industry verticals - Financial Services, IT, FMCG, Energy, Industrial Goods and across functional verticals - Strategy, Operational Transformation, Sales and Marketing. My role required formulation of hypotheses, conducting market research & data acquisition to build models, and synthesis, creation & effective roll-out of implementation plan.

Texas instruments

Intern - Digital Electronics

Jan 2013 – Jan 2013 · 0 mo · Bengaluru, Karnataka, India

I developed a model to measure and analyze power consumption of a SoC using 6 methods at different stages of SoC design flow using Synopsis Primetime, for signal activity, net parasitic and netlist data to improve and achieve power estimation accuracy ≈ 94%