In this talk, Tatiana, Staff Software Engineer at LinkedIn, shares her journey from academic physics to becoming a Kaggle Master and winning the Sound Demixing Challenge. We explore how to use machine learning competitions as a strategic tool to build a high-impact career and bridge the gap between theory and production.You’ll learn about:

Turning competition code into professional GitHub repos.
Converting results into papers for NIPS and CVPR.
How LLMs are changing the benchmark for AI competitions.
Why hands-on implementation beats passive learning.
Using Topcoder and AI Crowd for research-driven goals.
Practical steps for your very first model submission.Links:
Rise: 3 Practical Steps for Advancing Your Career, Standing Out as a Leader, and Liking Your Life. By Patty Azzarello https://www.porchlightbooks.com/pages/author/Patty_Azzarello-16156396 - awesome book about why doing good is not enough, and what else you need to do to promote your career (same applies to competitions)
AICrowd - https://www.aicrowd.com/challenges
Grand challenges - https://grand-challenge.org/challenges/
Kaggle competitions - https://www.kaggle.com/competitions
TopCoder challenge SpaceNet 9 - https://www.topcoder.com/challenges/9620f66a-767e-40ac-81d5-5cc61274b186(no current active competitions, but they appear)
Medium blog post with instruction - https://medium.com/data-science/writing-papers-tech-reports-after-kaggle-competitions-ee504fc0c4c1
Kaggle Solution Write-Up Documentation - https://www.kaggle.com/solution-write-up-documentation
Evaluating Machine Learning Agents on Machine Learning Engineering - https://arxiv.org/abs/2410.07095
Machine Learning Engineering Agent via Search and Targeted Refinement - https://arxiv.org/html/2506.15692v2
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench - chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2507.02554TIMECODES:00:00 Tatiana’s journey from academia to staff software engineer06:01 Machine learning applications in physics and signal processing09:13 Skill development and domain diversification on Kaggle13:35 Agentic AI benchmarks and automated competition entries17:43 Deep technical mastery versus leaderboard gamification23:04 Hands-on implementation and the illusion of learning26:01 Specialized platforms and fair competition environments31:35 Academic publications and research from silver medals35:24 GitHub repositories and engineering portfolio building39:02 Technical marketing via blog posts and LinkedIn43:25 Innovative approaches for academic conference submissions47:21 Research challenges at NIPS and CVPR workshops52:51 Medical imaging platforms and specialized recommendations57:46 First submission strategies for beginners01:00:56 Asynchronous collaboration and competition team dynamicsPerfect for data scientists and engineers looking to transition from academia or build a formal portfolio using Kaggle as a career-advancement tool.Connect with Tatiana:
Linkedin - https://www.linkedin.com/in/tatigabru/