Meet HackerNoon Contributor, Misam Abbas: The LinkedIn Engineer Building Trustworthy AI Systems

Written by misamabbas | Published 2025/10/28
Tech Story Tags: ai-adoption | ai-trustworthiness | meet-the-writer | trustworthy-ai-systems | ai-temperature-setting | llm-explainability | ai-diversity-and-ethics | hackernoon-contributors

TLDRIn this Meet the Writer interview, LinkedIn Staff AI Engineer Misam Abbas shares his journey from Meta and Dropbox to building trustworthy AI systems that balance ethics, diversity, and innovation. He discusses his writing process, fascination with AI temperature tuning, and the importance of explainability in large language models. Beyond code, Abbas champions mentorship, storytelling, and widening community participation in AI’s evolution.via the TL;DR App

Welcome to HackerNoon’s Meet the Writer Interview series, where we learn a bit more about the contributors that have written some of our favorite stories.


So let’s start! Tell us a bit about yourself—for example, name, profession, and personal interests.

Hello!

My name is Misam Abbas, and I am a Staff AI Engineer at LinkedIn. I have previously worked with Meta and Dropbox. I have around 10 years of experience in Machine Learning and its applications. I enjoy the process of building trustworthy AI systems, starting from first principles of understanding the product use cases, setting up appropriate evaluations, developing models, running experiments, and supporting post-deployment monitoring. I am particularly interested in the ecosystem effects of large-scale ML systems, e.g., how a recommendation sourcing strategy affects the diversity of content, what users find valuable in AI-driven products, what common issues are encountered, and so on.

For personal interests, I am a voracious reader with particular interests in science fiction, physics, philosophy, neuroscience, and ethics. I like to understand the fundamentals of the disciplines I am interested in.

Interesting! What was your latest Hackernoon Top story about?

My latest story, What Happens When You Change the ‘Temperature’ of Your AI?, is about how altering the temperature settings when invoking an LLM alters the outputs. The article explains how the LLMs output probability distributions over a token vocabulary - and sampling is needed to get an actual text output stream. Further, I discuss how temperature lets us control how we want to sample, depending on the use case. It is directed towards practitioners and also includes some mathematical background.

Do you usually write on similar topics? If not, what do you usually write about?

I do like to write about the practical concerns while developing applications with LLMs (this is my current area of focus). For example, recently I published an article on LLM Evaluations at another publication.

Great! What is your usual writing routine like (if you have one?)

When I want to write about something, I first try to find out if there is a real need for this kind of article. If I see there’s already a lot of high-quality articles on a topic (e.g., prompt tuning), I usually avoid that topic unless I have something unique to add. Then, after choosing a topic, I dive deep into researching the topic (sometimes for weeks) - this also gives an opportunity to crystallize my understanding.

I then write the first draft, which is not very polished but sets the general tone. Sometimes I need a bit more research after that.

Then it’s a few iterations and getting feedback from family and friends who are in the intended audience. In the end, when I am happy with the article, I start looking for appropriate publications.

Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing?

Just allocating time to writing without being distracted by other tasks at hand. Building the initial momentum can be hard, but once the first draft is done, things usually move faster.

What is the next thing you hope to achieve in your career?

That’s a great question! I want to continue building Trustworthy AI Applications. Additionally, I want to help others do the same. Given my years of experience across multiple industries and geographies, I think I can add value to the conversations around the changing industry landscape with AI adoption. I will continue engaging with the industry through conference talks and publications

I also want to help individuals (through formal and informal mentorships) and companies navigate this changing industry landscape driven by AI advances.

Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice?

I do like to binge-watch TV Series at times. “Dark Matter” and “Severance” on Apple TV are recent favorites.

I like to write short stories, often science fiction. Readership is mostly limited to family and friends whom I can coerce into reading. But maybe after retirement, I will publish a collection of short stories - or who knows - maybe a novel 😀

What can the Hacker Noon community expect to read from you next?

I am toying with the idea of accessible and short overviews of the greatest-hit papers from the field of AI that have had a huge influence on the field. Another idea is something on AI explainability.

What’s your opinion on HackerNoon as a platform for writers?

As a writer, I appreciate that HackerNoon gives creative and technical freedom. It values substance over being super polished. As a reader, I appreciate the thoughtful essays, experiments, or in-progress ideas. The community feels authentic, and the editors genuinely care about good storytelling around technology.

Thanks for taking the time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words?

I am optimistic about the role that AI has to play in society. I also strongly believe that it is important to get wider community participation in the field of AI, as that will enhance the chances of good outcomes. I hope to contribute to this effort.


Written by misamabbas | Misam Abbas is a Staff AI Engineer at LinkedIn, currently working on GenAI Safety with over a decade of experience building large-scale ML systems,
Published by HackerNoon on 2025/10/28