Within the Writer Highlight sequence, TDS Editors chat with members of our group about their profession path in knowledge science and AI, their writing, and their sources of inspiration. Immediately, we’re thrilled to share our dialog with Sara Nobrega.
Sara Nobrega is an AI Engineer with a background in Physics and Astrophysics. She writes about LLMs, time sequence, profession transition, and sensible AI workflows.
You maintain a Grasp’s in Physics and Astrophysics. How does your background play into your work in knowledge science and AI engineering?
Physics taught me two issues that I lean on on a regular basis: methods to keep calm after I don’t know what’s occurring, and methods to break a scary drawback into smaller items till it’s not scary. Additionally… physics actually humbles you. You study quick that being “intelligent” doesn’t matter should you can’t clarify your pondering or reproduce your outcomes. That mindset might be essentially the most helpful factor I carried into knowledge science and engineering.
You latterly wrote a deep dive into your transition from an information scientist to an AI engineer. In your each day work at GLS, what’s the single greatest distinction in mindset between these two roles?
For me, the largest shift was going from “Is that this mannequin good?” to “Can this technique survive actual life?” Being an AI Engineer isn’t a lot concerning the excellent reply however extra about constructing one thing reliable. And actually, that change was uncomfortable at first… but it surely made my work really feel far more helpful.
You noted that whereas an information scientist would possibly spend weeks tuning a mannequin, an AI Engineer may need solely three days to deploy it. How do you steadiness optimization with pace?
If now we have three days, I’m not chasing tiny enhancements. I’m chasing confidence and reliability. So I’ll give attention to a stable baseline that already works and on a easy strategy to monitor what occurs after launch.
I additionally like transport in small steps. As a substitute of pondering “deploy the ultimate factor,” I believe “deploy the smallest model that creates worth with out inflicting chaos.”
How do you suppose we may use LLMs to bridge the hole between knowledge scientists and DevOps? Are you able to share an instance the place this labored effectively for you?
Knowledge scientists converse in experiments and outcomes whereas DevOps of us converse in reliability and repeatability. I believe LLMs will help as a translator in a sensible manner. As an example, to generate exams and documentation so what works on my machine turns into “it really works in manufacturing.”
A easy instance from my very own work: after I’m constructing one thing like an API endpoint or a processing pipeline, I’ll use an LLM to assist draft the boring however essential components, like take a look at circumstances, edge circumstances, and clear error messages. This hastens the method loads and retains the motivation ongoing. I believe the secret’s to deal with the LLM as a junior who’s quick, useful, and infrequently incorrect, so reviewing every thing is essential.
You’ve cited research suggesting a large development in AI roles by 2027. If a junior knowledge scientist may solely study one engineering talent this yr to remain aggressive, what ought to it’s?
If I needed to decide one, it might be to discover ways to ship your work in a repeatable manner! Take one undertaking and make it one thing that may run reliably with out you babysitting it. As a result of in the true world, the most effective mannequin is ineffective if no one can use it. And the individuals who stand out are those who can take an thought from a pocket book to one thing actual.
Your current work has centered closely on LLMs and time sequence. Trying forward into 2026, what’s the one rising AI matter that you’re most excited to write down about subsequent?
I’m leaning increasingly more towards writing about sensible AI workflows (the way you go from an thought to one thing dependable). In addition to, if I do write a couple of “sizzling” matter, I need it to be helpful, not simply thrilling. I wish to write about what works, what breaks… The world of knowledge science and AI is stuffed with tradeoffs and ambiguity, and that has been fascinating me loads.
I’m additionally getting extra interested in AI as a system: how totally different items work together collectively… keep tuned for this years’ articles!
To study extra about Sara’s work and keep up-to-date together with her newest articles, you possibly can comply with her on TDS or LinkedIn.
