EfficientML + MLOps Specialist | PyTorch Core Contributor | Lightweight AI Evangelist

Minjae Ryu

Professional Summary

Engineer focused on scalable and efficient machine learning. Contributed to PyTorch internals and open-source tools for model pruning and optimization. Deep experience with distributed ML training, CUDA-level profiling, and end-to-end deployment pipelines using open source DevOps tools. Active in learning communities and research-driven engineering teams.

Work Experience

at PyTorch (Open Compute Foundation)

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Designed Taylor-series-based unstructured pruning for torch.nn.utils (#157620). Added modular test support for BatchNorm2d, LayerNorm, GroupNorm (#157613). Refactored CUDA kernel launcher for enhanced profiling (#156016). Extended enum handling in backend runtime (#156004).

Education

in Applied Linear Algebra for ML

Pusan National University

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Skills

Technical Skills

  • Python
  • PostgreSQL
  • Bash
  • PyTorch
  • Gradio
  • WandB
  • TorchProfiler
  • GitHub Actions
  • Airflow
  • Ruff
  • n8n
  • Pytest

Certifications

Vision Systems Accelerator

InnoVision Korea

Machine Learning Foundations

AIFrontier (Coursera)

Score
75/100
Work Experience
70/100
Certifications
80/100
Projects
75/100
Profile
78/100
Skills
65/100
Header
80/100
Education
60/100
Add explicit job titles and employment types in work experience for better ATS parsing.
Include measurable achievements and quantitative impact statements across work and project sections.
Expand education details to include degree type, graduation dates, and GPA if applicable.
Enhance skills section with soft skills and organize technical skills into clear subcategories.
Provide links to projects and certifications to support claims and increase recruiter trust.
Strong technical expertise demonstrated through contributions to PyTorch internals and open-source ML tools.
Active engagement in community knowledge sharing and speaking engagements.
Relevant certifications that validate machine learning and AI proficiency.
Experience with scalable ML pipelines, CUDA profiling, and MLOps automation.

Work Experience

70%
Work experience includes detailed technical contributions with references to issue numbers, demonstrating active engagement.
Add explicit job titles and employment types to clarify roles and improve ATS parsing.
Include quantifiable results or impact statements (e.g., performance improvements, user adoption) to enhance recruiter appeal.
Format dates consistently and consider adding bullet points to improve readability.

Certifications

80%
Certifications are relevant and include issuing authorities and dates, which supports credibility.
Add credential IDs if available and links to certification verification to improve trustworthiness.
Briefly highlight key skills or knowledge gained from each certification in bullet points.
Consider adding more recent or advanced certifications related to AI/ML to boost competitiveness.

Projects

75%
Projects are well described with technical details and contributions to open-source initiatives.
Include measurable outcomes or impact to quantify project success.
Add links to project repositories or demos to enhance recruiter engagement.
Use bullet points for easier readability and ATS scanning.

Profile

78%
Summary is concise and highlights key technical skills and contributions, which is attractive to recruiters.
Add specific measurable achievements or impact metrics to increase credibility and recruiter interest.
Consider adding a distinct 'Profile Highlights' or 'Core Competencies' section with relevant keywords like 'distributed ML', 'CUDA profiling', and 'DevOps pipelines' for better ATS optimization.

Skills

65%
Technical hard skills are well listed with relevant tools and programming languages.
Add soft skills and tools categories with examples such as 'team collaboration' or 'project management' to present a balanced skill set.
Use keyword-rich phrases like 'machine learning model optimization' or 'MLOps automation' to improve ATS keyword matching.
Consider organizing skills into subcategories for clarity and easier parsing.

Header

80%
Header includes clear name, professional headline, and contact information, which is good for ATS parsing.
Add a formal job title or remove ambiguous terms like 'EfficientML + MLOps Specialist' to improve keyword matching.
Include a standardized location format with city and country explicitly stated to help location-based ATS filters.

Education

60%
Education section includes relevant coursework but lacks degree information, which can reduce ATS impact.
Add degree type, graduation year, and GPA if applicable to strengthen this section.
Expand on educational achievements or relevant projects to demonstrate applied knowledge.
Use consistent date formatting and consider adding location for the institution.