Michael Henning, Developer in New York, NY, United States
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Michael Henning

Verified Expert  in Engineering

Performance Tuning Developer

Location
New York, NY, United States
Toptal Member Since
June 28, 2021

Michael's professional experience includes deep learning and computer vision at Facebook and a venture-funded startup. He enjoys freelancing because of the flexibility and variety it provides. Michael is looking forward to working with clients who can use his specialized skills in deep learning and performant systems, as well as clients who need more general back-end development expertise.

Portfolio

Facebook
C++, Python, PyTorch
GrokStyle
C++, Python, PyTorch, Django, Amazon Web Services (AWS), Google Cloud
Google
C++, Image Processing

Experience

Availability

Part-time

Preferred Environment

Linux, Visual Studio Code (VS Code), Git

The most amazing...

...improvement I've made to a production system cut search cost and latency by a factor of four at a startup where the search was our core focus.

Work Experience

Computer Vision Software Engineer

2019 - 2021
Facebook
  • Debugged and configured a production image inference system in C++ that processes thousands of queries per second. Solved a problem that negatively impacted accuracy for all image models and proactively caught a subtle use-after-free bug.
  • Contributed research directions and code to GrokNet, a model for fine-grained image recognition across several business verticals, including developing a novel way to aggregate contrastive loss pairs across GPUs.
  • Co-developed SimSearchNet++, a model for near-duplicate image detection.
Technologies: C++, Python, PyTorch

Software Engineer

2018 - 2019
GrokStyle
  • Improved throughput of image loading by over eight times to support multi-GPU training.
  • Led migration of the core training and inference stack from Caffe to PyTorch.
  • Cut search cost and latency by four times by finding and implementing optimization opportunities in a core search routine, including replacing a previous BLAS-accelerated implementation with a custom C++ implementation.
Technologies: C++, Python, PyTorch, Django, Amazon Web Services (AWS), Google Cloud

Software Engineering Intern

2017 - 2017
Google
  • Ported an image super-resolution algorithm to run on custom coprocessors.
  • Leveraged symmetries to fit large lookup tables into 16KB of on-chip memory.
  • Wrote a fixed-point square root approximation which achieved a two-time speedup.
Technologies: C++, Image Processing

GrokNet

I was integral to the development of a model for fine-grained image recognition across several business verticals.

My contributions included writing a training system in Python that could effectively load images fast enough to maintain high GPU utilization during distributed model training runs while maintaining high developer flexibility, developing a novel way to aggregate contrastive loss pairs across GPUs that improved model accuracy, and catching several discrepancies between test systems and production which impacted the final accuracy of the model.

SimSearchNet++

https://ai.facebook.com/blog/heres-how-were-using-ai-to-help-detect-misinformation
I contributed accuracy and speed improvements to a neural network used for duplicate image detection, including comparisons between a variety of different architectures and quantization into int8. I was also involved in the deployment of this model to production traffic, where I proactively caught a subtle issue that caused early load testing results to underestimate resource usage by order of magnitude and also managed to track down a subtle bug related to receiving different results for a fraction of a percent of production traffic which ended up being related to differences in CPU microarchitectures in our server cluster.
2014 - 2018

Bachelor's Degree in Computer Science

Cornell University - Ithaca, NY, USA

Libraries/APIs

PyTorch, OpenGL

Tools

Git

Languages

C++, C, Python, Rust, Python 3, Java, Go, OCaml

Frameworks

Django, Presto

Storage

Google Cloud, PostgreSQL

Paradigms

Functional Programming

Platforms

Linux, Visual Studio Code (VS Code), Amazon Web Services (AWS)

Other

Memory Management, Performance Tuning, Deep Learning, Convolutional Neural Networks (CNN), Image Processing

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