Shubhankar Kahali
RSS FeedI'm an applied ML and security engineer who started in offensive security research and still stays hands-on through malware analysis and bug bounty work with Google, Apple, Meta, Microsoft, Amazon, and Uber. That background gives me a precise read on how strong engineers think and build.
My work spans ML research and executive search. I build production foundation models focused on sequence understanding—working with attention mechanisms, few-shot adaptation, and transformer architectures that perform reliably under real-world constraints in aerospace, defense, and cybersecurity. I'm particularly interested in how these models will transform human-computer interaction for complex analytical work. On the talent side, I identify and place exceptional engineers at Fortune 500 companies, applying the same systematic approach I use in ML and security. I'm expanding this work beyond tech into executive roles across industries.
My weekend side project, 
, demonstrates these principles in practice—a transformer-driven career platform I built to explore multi-agent architectures at scale. The system runs on distributed microservices with real-time ML pipelines, combining ensemble methods and deep collaborative filtering to process hundreds of career signals. With 39k+ active users and 94% placement accuracy, it's generated $21.9M+ in career value across 5,605+ successful matches—proving that thoughtful ML architecture scales.
Featured
Inverse Scaling Laws in Neural Networks
Published: at 09:19 AMA comprehensive examination of inverse scaling in large language models, where increased computational power paradoxically leads to degraded performance on specific tasks, challenging our fundamental assumptions about AI progress.
Why Complex Systems Can't Be Designed
Published: at 12:08 PMWhy the biggest systems started as the smallest ones, and what Gall's Law teaches us about building things that actually work.
Teaching Machines to Think Like Machines
Published: at 02:42 PMHow RASP lets us program transformers the way they actually think, bridging the gap between neural networks and human understanding of computation.
Finding True Intelligence in Language Models
Published: at 04:54 AMWhy autoregressive language models might be more parlor trick than true intelligence, and how the search for meaningful latent representations could transform how AI understands language.
The Environmental Ceiling You Never See
Published: at 03:43 AMHow your environment silently limits your potential, and why changing your surroundings might be the most important decision you'll ever make.
The Hidden Career Advantage No One Talks About
Published: at 09:07 AMWhy the most uncompetitive career paths are the ones that require emotional discomfort, and how embracing the difficult feelings everyone else avoids can be your greatest competitive advantage.
Data's Journey to Wisdom
Published: at 11:00 AMA deep dive into how raw data transforms into actionable wisdom, and why understanding this journey is crucial for both individuals and organizations in our data-driven world.
Making AI Think Faster Without Getting Sloppy
Published: at 07:42 PMA deep dive into how we slashed AI response times using Chain-of-Thought prompting and few-shot learning, with real implementation examples and practical insights from the trenches.
Recent Posts
The People Your Process Can't See
Published: at 11:54 AMThe quiet builders who hold companies together rarely fit your performance matrix. Here's how to see, reward, and keep them before it's too late.
Bootstrapping Q
Published: at 04:54 AMA model-in-the-loop playbook for turning a low-resource language into a usable domain.
People measure your worth by their own metric
Published: at 04:34 AMWhy the way people measure your worth says more about them than you—and how understanding someone's metric for self-worth is the real key to compatibility.
When Languages Fight for Neural Territory
Published: at 01:39 PMDeep dive into dynamic mixture-of-experts for multilingual LLMs - how measuring parameter deviation reveals hidden language relationships and solves the curse of multilinguality through intelligent resource allocation.