Tim Esler
Senior Principal Machine Learning Engineer at Atlassian
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolTim Esler is a senior principal machine learning engineer and leader with a PhD in computational neuroscience who has spent a decade turning deep research into production AI systems. He has led ML teams at companies from healthcare startups to AWS, where he architected and launched Amazon’s central generative-AI knowledge discovery service using Glue, OpenSearch, SageMaker, and Bedrock. At Seer he ran engineering for clinical AI products, and earlier roles span cybersecurity, public-safety, and high-performance analytics—demonstrating a rare mix of domain breadth and hands-on delivery. Tim is the creator of the widely used facenet-pytorch library, translating TensorFlow face models into a PyTorch ecosystem that many practitioners rely on. He combines people leadership and MLOps rigor with deep technical authorship, often taking complex prototypes from concept to production in under a year. Based in Seattle, he brings academic depth in neural engineering to pragmatic, scalable ML systems.
10 years of coding experience
8 years of employment as a software developer
Certificate IV of Business Administration Business, Certificate IV of Business Administration Business at UpSkillHSC General, HSC General at Xavier High School AlburyThe University of Melbourne
Stackoverflow
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Github Skills (7)
face-detection10
computer-vision10
pytorch10
machine-learning10
tensorflow10
python10
face-recognition10
Programming languages (4)
Github contributions (5)
timesler/facenet-pytorch
May 2019 - Dec 2021
Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
Role in this project:
Back-end & ML Engineer
Back-end & ML EngineerContributions:8 releases, 10 reviews, 238 commits in 2 years 7 months
Contributions summary:Tim primarily contributed to integrating Tensorflow models into a Pytorch environment for face detection and recognition. They implemented functions to import and load Tensorflow model parameters into PyTorch models, particularly within the InceptionResNetV1 architecture. Further development included creating and integrating the MTCNN face detection pipeline. The user's work focused on converting existing models and systems for improved functionality and workflow.
pytorchpretrainedface-recognitionmtcnn-face-detectionrecognition
timesler/lr-momentum-scheduler
Jun 2019 - Jul 2020
Pytorch implementation of arbitrary learning rate and momentum schedules, including the One Cycle Policy
Contributions:14 commits, 2 PRs, 11 pushes in 1 year 1 month
pytorchmomentumpolicylearning-ratedeep-learning
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