Kamran Jafari
Data AnalystAI 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
GenAI @ NVIDIA | ex-Cerebras Systems | ex-Intel | PhD, University of Toronto
I'm a Senior GenAI Algorithms Engineer at NVIDIA, working on the algorithms behind large language model training and inference. Over the last decade I've built and optimized deep neural networks at companies pushing the frontier of AI compute: 5+ years at Cerebras Systems as a Principal Applied ML Scientist and Tech Lead Manager (leading LLM work on the world's largest AI accelerator), and earlier roles as a Senior ML Developer at Borealis AI and a Deep Learning Engineer at Intel working on FPGA-based AI solutions. My toolkit centers on PyTorch, CUDA, C/C++, vLLM, and SGLang, with a research foundation from a PhD in Electrical and Computer Engineering from the University of Toronto. Reach out if you want to talk LLM performance, model optimization, or AI hardware/software co-design.
University of Toronto
Doctor of Philosophy (PhD), Electrical, Electronics and Communications Engineering
January 1, 2012 – January 1, 2017
Shiraz University
Master of Science (M.Sc.), Electrical, Electronics and Communications Engineering
January 1, 2009 – January 1, 2012
Shiraz University
Bachelor of Science (B.Sc.), Electrical, Electronics and Communications Engineering
January 1, 2005 – January 1, 2009
NVIDIA
Senior GenAI Algorithms Engineer
January 1, 2026 – Present
Santa Clara, California, United States
Cerebras Systems
Principal Applied ML Scientist | Tech Lead Manager
August 1, 2022 – October 1, 2025
Cerebras Systems
Staff Applied Machine Learning Scientist
May 1, 2020 – August 1, 2022
Borealis AI
Senior Machine Learning Developer
November 1, 2018 – May 1, 2020
Toronto, Ontario, Canada
Intel Corporation
Deep Learning Software Engineer
September 1, 2017 – November 1, 2018
Toronto, Ontario, Canada
Advanced Mobile Payment Inc.
Embedded Software Engineer
September 1, 2013 – August 1, 2017
Richmond Hill, Canada
Large-scale server-based indoor positioning system for Android smart phones
July 1, 2016 – Present
An indoor positioning system based on Wi-Fi fingerprinting was developed. The project included a server connection to SQL database which was holding the Wi-Fi fingerprints and an Android smart phone as the localization client.
Indoor localization of Android smart phones using TI CC2540 BLE beacons
August 1, 2015 – Present
In this project, BLE beacons were programmed to broadcast their locations. Then we developed an Android application to localize the smart phone using these beacons and show the location on Google Maps.
Android Sensors Acquisition/Transmission System
May 1, 2014 – Present
We developed an Android application to collect the samples of all Android sensors and transmit them to a PC running MATLAB via TCP/IP. The data could be used on the PC in real-time to implement signal processing algorithms.
Cultural Fit Analysis
The candidate's experience is heavily skewed towards Machine Learning, Deep Learning, and Generative AI, with roles at companies like NVIDIA, Cerebras Systems, Borealis AI, and Intel. The target role is 'Data Analyst'. This represents a significant mismatch. While the candidate possesses strong analytical skills from their ML background, the specific focus and responsibilities of a Data Analyst (e.g., SQL, data visualization, business intelligence, specific data analysis tools) are not explicitly highlighted in their experience or projects. The projects listed are more aligned with embedded systems and mobile sensor data, which are not directly relevant to a typical Data Analyst role.
Soft Skills & Operational Fit
The candidate's career progression to Principal Applied ML Scientist and Tech Lead Manager roles suggests strong leadership, problem-solving, and potentially good communication skills. However, without specific psychometric test results or interview data, a definitive assessment of soft skills and operational fit is limited.