Max Lowther
Senior Software Engineer at Meta
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMax Lowther is a Senior Software Engineer with 11 years of experience building large-scale, production-grade systems across ML deployment and distributed simulation domains. Currently at Meta working on trust & safety for roughly 4 billion daily active users, he brings deep expertise in reliability, secure communications, and scalable backend engineering. Prior roles at Seldon saw him lead enterprise platform efforts and contribute notable MLOps enhancements—such as multi-request batch prediction handling and SSL support—to the widely used seldon-core open-source framework. His background includes systems work in Golang, gRPC, Kubernetes and cloud platforms from Improbable, complemented by a strong academic foundation from Cambridge and MIT. Colleagues describe him as a pragmatic engineer who pairs hands-on coding with team leadership, often surfacing subtle interoperability and observability improvements before they become issues. He consistently bridges research-grade ML tooling and production constraints to make model deployment reliable and secure.
11 years of coding experience
6 years of employment as a software developer
Computer Science, Computer Science at Massachusetts Institute of TechnologyMaster's Degree Information and Computer Engineering, Master's Degree Information and Computer Engineering at University of CambridgeHigh School, High School at Bishop Luffa Comprehensive School
English, German
Github Skills (16)
kubernetes10
machine-learning10
deploying10
mlops10
python10
kubernetes-pods10
rest-api9
json8
testing8
numpy8
dockers7
docker7
tensorflow6
ml5
aiml5
Programming languages (14)
Github contributions (5)
SeldonIO/seldon-core
Jun 2021 - Jan 2023
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Role in this project:
MLOps Engineer
MLOps EngineerContributions:24 reviews, 33 commits, 16 PRs in 1 year 7 months
Contributions summary:Max primarily contributed to the `seldon-core` repository by implementing and refining batch processing capabilities. Their work involved modifications to the `batch_processor.py` file, focusing on enhancements for multi-request batch prediction, including the handling of different data types and payload formats such as `ndarray` and `tensor`. These changes included adjustments to the `_send_batch_predict_multi_request` function to handle batch sizes greater than one, along with the addition of testing. They also integrated SSL support for secure REST communication and incorporated changes related to proper tag propagation with the Seldon client.
monitorservingdata-sciencedeploymentmachine-learning
majolo/majolo.github.io
Apr 2015 - May 2020
Contributions:11 pushes, 1 branch in 5 years 2 months
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