Dheeraj Peri
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior Deep Learning Engineer at NVIDIA
I'm a self-motivated individual, extremely passionate about Deep Learning and its applications in Computer Vision and Reinforcement Learning. I am highly fascinated by the advancements in deep learning in the recent years and how it enables machines to learn complicated patterns better than humans. My specific interests include - Multi-modal learning, Information Retrieval - Unsupervised learning (Generative Adversarial Networks, Adversarial Machine Learning) - Reinforcement Learning I worked on optimization of object detection networks and intelligent character recognition using fully convolutional networks during my internships at NVIDIA and Kodak Alaris. Recently I have completed a Deep Reinforcement Learning Nanodegree from Udacity. Prior to graduate study at RIT, I worked as a Product Analyst at Phenom people. Key Skills : Python, TensorFlow, Caffe, C++, PyTorch, CUDA. Github: https://github.com/peri044
Udacity
Deep Reinforcement Learning Nanodegree, Computer Science
January 1, 2018 – January 1, 2018
Rochester Institute of Technology
Master's degree, Computer Engineering
January 1, 2016 – Present
Birla Institute of Technology and Science, Pilani
Undergraduate, Electrical and Electronics Engineering
January 1, 2011 – January 1, 2015
NVIDIA
Senior Deep Learning Engineer
January 1, 2019 – Present
Santa Clara, California, United States
NVIDIA
Deep Learning Intern
February 1, 2018 – June 1, 2018
Santa Clara
Kodak Alaris
Deep Learning Software Intern
May 1, 2017 – December 1, 2017
Rochester, New York Metropolitan Area
Rochester Institute of Technology
Deep learning Researcher
January 1, 2017 – December 1, 2018
Rochester, New York
Phenom People
Product Analyst
July 1, 2015 – July 1, 2016
Hyderabad
Healthcare Technology Innovation Centre, IIT Madras
Research Internship
January 1, 2015 – June 1, 2015
Chennai
Gaussian Elimination using CUDA
September 1, 2016 – Present
Designed a parallel algorithm for solving a large system of equations using Gaussian Elimination using CUDA. Improved the execution times by efficient allocation of GPU resources.
Fantasy points prediction in NFL using Machine Learning Techniques
August 1, 2016 – Present
Quarterback is the most important player in American Football. In this project, predictions of the fantasy points of a quarterback were made using SVM and Neural networks. The predictions were based on past game and season's performance. Presented a poster on NFL predictions at Western New York Signal and Image processing Workshop.
Parallel Sorting Algorithms on GPU
August 1, 2016 – Present
Implemented sorting algorithms on GPU which performed much better than sequential sorting and resulted in higher speed up. Bitonic Sort was the focus of this project.
Parallel implementations of Image processing Algorithms
August 1, 2016 – Present
Median Filtering on images and clustering algorithms were implemented using CUDA. This project helped me realize the extent of parallelism in Computer Vision applications and that can be exploited further.
Cultural Fit Analysis
The candidate's career path shows a strong focus on Deep Learning and parallel computing, aligning well with a specialized ML Engineer role. Their experience at NVIDIA, a leader in the field, suggests a fit for high-performance, innovation-driven environments. The personal projects demonstrate initiative and a passion for the domain. However, the lack of diverse project types outside of core ML/parallel computing, and the absence of explicit team collaboration or leadership descriptions in the project section, make it difficult to fully assess broader cultural fit aspects like cross-functional collaboration or mentorship potential beyond technical leadership.
Soft Skills & Operational Fit
The candidate's experience at NVIDIA as a Senior Deep Learning Engineer and their contributions to significant projects like Nemotron-Ultra and Torch-TensorRT suggest strong problem-solving abilities, technical leadership, and the capacity to work on complex, cutting-edge research and development. The description of building and evaluating agentic systems implies an ability to work with novel and evolving technologies. The project descriptions, while brief, indicate an understanding of performance optimization and algorithm design.