Phil Gaisford
Billerica, Massachusetts, United States
Summary
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RockstarPhil Gaisford is a seasoned software engineer with over twenty years of experience building and supporting production systems in mission-critical environments, now focused on backend and MLOps engineering. Based in Billerica, Massachusetts, he combines strong customer-facing communication skills with a track record of delivering high-quality results under pressure, whether working independently or as part of a team. His recent open-source contributions to Determined AI demonstrate practical expertise integrating HPC schedulers like PBS and Slurm, improving resource management and checkpoint handling for distributed ML workloads. Phil’s strengths lie in modifying core platform components and clarifying complex configurations through thoughtful refactoring and documentation updates. Known for reliability and pragmatic problem-solving, he bridges operational realities and developer needs to keep experiments and services running smoothly. An often-overlooked asset is his ability to translate operational constraints into clean backend changes that reduce failure modes in production.
3 years of coding experience
german, swiss german, English
Github Skills (16)
mlops10
distributed-training10
backend10
slurm10
back-end-development10
python10
pbs10
deeplearning-ai9
machine-learning9
deep-learning9
go9
kubernetes8
data-science8
kubernetes-pods8
dockers7
Programming languages (2)
Github contributions (3)
determined-ai/determined
Aug 2022 - Jan 2023
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Role in this project:
MLOps & Backend Engineer
MLOps & Backend EngineerContributions:101 reviews, 14 commits, 97 PRs in 4 months
Contributions summary:Phil contributed to the Determined AI platform by implementing features related to HPC cluster integration, specifically focusing on PBS and Slurm resource management. Their work involved modifying core components to support PBS, refactoring options for HPC cluster configurations within experiment configurations, and updating documentation to reflect the new functionality. They also addressed checkpointing issues, ensuring proper handling of experiments with zero checkpoints. This user demonstrated expertise in integrating with HPC cluster schedulers and modifying the backend for resource management.
pytorchdata-sciencedeep-learningdistributed-trainingmachine-learning
phillip-gaisford/determined
Aug 2022 - Jul 2023
Determined: Deep Learning Training Platform
Contributions:2 PRs, 105 pushes, 83 branches in 10 months
deep-learningpytorchmachine-learningtraining
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