Daniel Coronado

AI/ML & Full-Stack Engineer | Co-founder

New York City, US.

About

Highly accomplished AI/ML and Full-Stack Engineer with a strong background in developing scalable, data-driven solutions and leading innovative projects. Proven expertise in machine learning infrastructure, full-stack development, and optimizing complex systems across diverse industries, including manufacturing, distributed systems, and computer vision. Adept at leveraging advanced technologies to drive efficiency and build impactful products.

Work

MACHINEFLOWS

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Co-founder/ Engineer

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Summary

Co-founded a startup during Antler NYC9 residency, focusing on AI-driven workflow automation for manufacturing.

Highlights

Selected for Antler NYC9 residency, leading the exploration and development of AI-driven workflow automation solutions for manufacturing, focusing on smart search and operational streamlining.

Architected and developed AI and OCR solutions to extract critical insights from blueprints and unstructured documents, significantly accelerating quoting workflows for manufacturing clients.

Spearheaded full-stack development initiatives, utilizing Python, TypeScript, React, FastAPI, AWS, Supabase, and PostgreSQL to build robust and scalable applications.

AMAZON WEB SERVICES (AWS)

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Software Engineer (Redshift – Distributed Systems)

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Summary

Engineered and optimized distributed systems for Redshift, enhancing efficiency and availability.

Highlights

Engineered a machine learning-based automated regression system, eliminating hundreds of hours of manual bug/feature ticket assignment weekly by autonomously tracking ticket lifecycles.

Led the architectural design and infrastructure development for a critical service, leveraging Python, TypeScript, and a suite of AWS services (Lambda, Redshift, DynamoDB, S3, SQS, SNS, CloudWatch).

Automated the cutting, de-duplication, and ownership assignment of customer tickets across over 20 AWS production regions, enhancing operational efficiency.

Optimized Redshift's concurrency scaling mechanism, achieving significant reductions in query response times and boosting system availability across AWS regions.

QUALCOMM

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Software Engineer (R&D Computer Vision – Multimedia)

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Summary

Contributed to machine learning infrastructure and data pipeline development for computer vision applications.

Highlights

Developed robust machine learning infrastructure and ETL pipelines to facilitate training of deep learning models for advanced image and video-based face recognition, authentication, and hand tracking.

Created comprehensive data pipelines for processing, annotating, and generating high-quality egocentric datasets, crucial for advancing XR hand tracking capabilities.

UCSD IMAGE OPTIMIZATION GROUP

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Research Assistant

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Summary

Conducted research on image restoration algorithms.

Highlights

Conducted research focused on optimizing parameters for advanced image restoration algorithms, including de-noising, deblurring, super-resolution, and compressed sensing techniques.

WAVE COMPUTING

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Machine Learning Engineer Intern (AI/ML)

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Summary

Interned in AI/ML, implementing and optimizing neural network architectures.

Highlights

Implemented and optimized variations of Region-based CNN and GAN neural network architectures for enhanced face recognition and object detection applications, utilizing PyTorch and TensorFlow.

Contributed to research on optimizing Deep Residual Networks training, focusing on flexpoint and dynamic fixed-point numerical formats for significant hardware acceleration.

Education

University of California – San Diego (UCSD)

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B.S. Electrical Engineering Grade:

Courses

NLP (Undergraduate)

Stochastic Process (Undergraduate)

Deep Learning (Graduate)

Statistical Learning (Graduate)

Recommender Systems (Graduate)

Linear Algebra (Graduate)

Skills

Programming Languages

Python, TypeScript, JavaScript, SQL.

Tools & Platforms

AWS, OpenAI, Anthropic, Git, Docker, PostgreSQL, MongoDB, Supabase, Vercel.

Frameworks & Libraries

FastAPI, Pydantic, React, React Native, Next.js, Express, PyTorch, Pandas.

Machine Learning & AI

Deep Learning, Computer Vision, Natural Language Processing (NLP), Image Restoration, Neural Networks, CNNs, GANs, OCR, Statistical Learning, Recommender Systems.

System Design & Architecture

Distributed Systems, ETL Pipelines, Cloud Infrastructure, Concurrency Scaling.