Nadia Yakimakha
Staff Software Engineer at Meta
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolNadia Yakimakha is a Staff Software Engineer in the Greater Seattle Area with eight years of experience building production-grade infrastructure for machine learning and distributed systems. At Meta Reality Labs and previously at Amazon SageMaker, she focused on managed compute environments for deep learning frameworks, container tooling, and reliable training infrastructure. Her open-source contributions to high-profile AWS projects (sagemaker-python-sdk, sagemaker-tensorflow-training-toolkit, and example notebooks) show hands-on expertise with TensorFlow, MXNet, Horovod, GPU tooling, and practical ML examples for real workloads. She combines devops sensibilities—Dockerfile and GPU test work—with model-focused engineering, ensuring compatibility and reproducible training across frameworks. Early career experience delivering robust, risk-sensitive enterprise software cultivated a strong attention to reliability and troubleshooting complex issues. Nadia pairs an applied CS master’s background with a track record of shipping reproducible, scalable ML tooling used by many teams.
8 years of coding experience
8 years of employment as a software developer
Master’s degree in Computer Science Applied Mathematics and Computer Science, Master’s degree in Computer Science Applied Mathematics and Computer Science at Belarusian State University
English, Russian
Github Skills (19)
pytorch10
docker10
python10
mxnet10
machine-learning10
dockers10
sagemaker10
amazon-sagemaker10
deep-learning10
tensorflow10
aws10
gpu10
jupyter-notebook10
data-science9
horovod9
Programming languages (4)
Github contributions (5)
aws/sagemaker-tensorflow-training-toolkit
Jun 2018 - Jun 2020
Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:1 release, 23 commits, 54 PRs in 2 years
Contributions summary:Nadia contributed significantly to the project by modifying Dockerfiles, specifically related to TensorFlow versions and GPU support within the SageMaker environment. Their work included updating package installations, configuring Horovod for distributed training, and adding integration tests to verify GPU device access within the container images. Furthermore, they were involved in integrating code changes related to merging branches and updating the package name and version within the project.
awsdeep-learningsagemakertensorflowdocker
aws/sagemaker-python-sdk
Feb 2018 - Jul 2020
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:9 releases, 60 commits, 133 PRs in 2 years 5 months
Contributions summary:Nadia's commits primarily focused on supporting specific versions of TensorFlow and MXNet within the SageMaker Python SDK. This included updating tests, refactoring code, and fixing typos to ensure compatibility. The user also contributed to the addition of features related to handling model server workers and setting up the infrastructure required for them.
amazon-sagemakermachine-learning-modelsawsmxnettensorflow
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