Amogh Patankar
Research Engineer
San Francisco, CA
Experiences
Company
Title
Location
Date
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Hedra
Research Engineer
San Francisco, CA
May 2025 - Present
- Researching and implementing model inference acceleration techniques, focusing on efficient 3DVAE and diffusion modeling.
- Building ML infrastructure to optimize system performance for distributed training and cloud inference, with simultaneously monitoring.
- Responsible for scalable and efficient training, deployment, and optimization of diffusion transformer (DiT) models.
- Improving video data preprocessing for model training, specifically for vision transformer (ViT) and diffusion transformer architectures.
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AMD
AI Applications Development Intern
Santa Clara, CA
Sep 2024 - Dec 2024
- Optimized generative AI workload (Llama-2, Llama-3, Stable Diffusion, etc.) execution on RyzenAI neural processing unit (NPU) using strategies such as caching, batching, quantization, as well as model and data parallelism.
- Benchmarked generative AI workloads on Ryzen and competitor hardware, specifically time to first token, and tokens/sec, i.e. latency and throughput, as metrics.
- Evaluated NPU latency and utilization during concurrent execution of generative AI workloads and high-fidelity AI effects (Microsoft Studio Effects).
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Marvell Inc.
Data Scientist Intern
Santa Clara, CA
Jun 2024 - Sep 2024
- Architected an end-to-end ETL pipeline using numpy, pandas, sklearn, Tableau, etc. Data was ingested using Apache and AWS Kinesis, and stored in SnowflakeDB, and AWS S3.
- Utilized generative pretrained transformer (GPT) models to generate synthetic data, leveraging parameter efficient fine-tuning (PEFT) techniques such as low-rank adaptation (LoRA).
- Implemented reinforcement learning (RL) and deep learning methods for DSP parameter optimization.
- Integrated large language models (LLM) and retrieval augmented generation (RAG) to automate hardware modeling process.
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Stanford University
Researcher
Palo Alto, CA
Jun 2023 - Jun 2024
- Performed data engineering, analysis, and visualization through various methods in Python and R. Developed unique statistical packages composed of chi-squared and Fisher tests.
- Led research teams mentored by Dr. Eric Gross, Dr. Latha Palaniappan, and Jin Long. Opioid overdose research published in British Journal of Anaesthesia.
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Amazon Web Services
Software Development Engineering Intern
Seattle, WA
Jun 2022 - Sep 2022
- Led architectural changes in Lex ASR (Automatic Speech Recognition) Services and DataHub, improving latency for conversational AI models.
- Performed cohort analysis to segment customer data in DataHub, enhancing Lex ASR schemas, and enabled compliant storage of all data using AWS Kinesis, S3, and Lambda.
- Applied time series analysis on AWS CloudWatch metrics to track and optimize the performance of ASR Service, accelerating customer request resolution by up to ~75%.
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Scripps Research
Research Intern
La Jolla, CA
Jun 2021 - Aug 2021
- Developed an R library to estimate genetic regulatory variation using a confidence interval estimation method.
- Implemented various statistical concepts like binomial distributions and parametric bootstrapping, and applied them to data from the Genotype Tissue Expression Project (GTEx).
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Inphi Corporation
Firmware Embedded Engineering Intern
Santa Clara, CA
Jul 2019 - Sep 2019
- Developed Python modules and a user interface to create and display data based on test options and feature selection.
- Developed firmware in C++ to parse data, removing duplicates based on timestamp & hex value, per modular architecture.
Education
University
Degree
Department
Date
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UC San Diego
M.S., Computer Science (AI/ML)
Jacobs School of Engineering
Sep 2023 - Mar 2025
- Growth Strategist for Perplexity AI
- Co-President of the UCSD CSE Graduate Student Council

UC San Diego
B.S., Data Science
Halicioglu Data Science Institute
Sep 2020 - Mar 2023