Forem: Eduardo Gade Gusmao

Technical Interview Report: Senior Software Engineer at Bloomberg - Spring Boot Stack (4 Rounds)

Eduardo Gade Gusmao — Fri, 20 Feb 2026 14:17:28 +0000

If you are prepping for a Senior Software Engineer role at Bloomberg, you'll want to buckle up.

I recently went through their 1-hour technical screening, and it was a dense, fast-paced session that touched on everything from high-level architecture to LeetCode-hard problem-solving.

Here is the breakdown of what to expect and the specific technical challenges I faced.

The Interview Structure:

The session was strictly timed at 60 minutes, divided into four distinct segments:

Project Deep Dive: A discussion on my previous experience and architectural decisions.
SQL Assessment: Relational database querying.
Data Structures & Algorithms (DSA): Live coding.
Spring Boot Practical: A hands-on look at REST API design.

Part 1: SQL - Aggregations and Filters

The SQL portion wasn't designed to trick me with obscure syntax, but rather to test my comfort with window functions or subqueries.

The Challenge:

Given an Employee table (id, department_id, salary) and a Departments table (id, name);
Find the department(s) with the highest average salary.

Key Constraints:

You must return only the max average.
If there is a tie between multiple departments, you must return all of them. Tip for others: Don't just use LIMIT 1. Make sure your solution accounts for ties by using a HAVING clause or a RANK() window function.

Part 2: DSA - The "Hard" Wall

The interview took a sharp turn into high-level algorithmic thinking. I was hit with Sliding Window Maximum (a well-known LeetCode Hard). The goal is to find the maximum value in a sliding window of size k as it moves across an array.

For a Senior role, they aren't looking for the 𝒪(nk) brute-force solution; they want to see if you can optimize it to 𝒪(n) using a Deque (Double-Ended Queue) to keep track of indices in a monotonic fashion. If you are rusty on monotonic queues, brush up before interviewing here.

Part 3: Spring Boot - Annotations and API Design

Because this role requires strong Spring Boot expertise, the final segment was a "sanity check" on my framework knowledge. I was asked to live-code a sample REST API.

Focus areas included:

Proper use of @RestController and @RequestMapping.
Dependency Injection via @Service and constructor-based injection.
Handling HTTP verbs and path variables.

Final Thoughts

Bloomberg seems to value "T-shaped" engineers-people who have the high-level Spring Boot context but haven't forgotten how to handle complex algorithms or raw SQL.

My advice? Practice your sliding window logic and ensure you can talk through your past architectural choices clearly and concisely.

I was mistaken for an AI. What should I do?

Eduardo Gade Gusmao — Sun, 25 Jan 2026 04:18:47 +0000

It Finally Happened: My Writing Was Tagged as AI Content

Part 1: The Incident

Let me paint you a picture. Stack Overflow—that digital colosseum where developers go to solve problems and occasionally lose faith in humanity—has some gold-standard unwritten rules:

There is only one right answer (yours better be it).
If I have more "reputation" than you, I am literally, objectively, measurably better than you.
Answers are not meant for learning, only problem-solving. No depth. No flourish. Just the fix.

I violated the third rule. The one I'm actually (fairly, given Stack Overflow's Darwinian culture) okay with.

The gentleman who flagged me was surprisingly kind. He offered genuinely good advice: "Provide references." He's right—LLMs rarely embed citations unless explicitly prompted. But here's the twist: I wrote that answer entirely myself.

See, I'm not neurotypical. My brain naturally produces what people now call "AI fluff"—the verbose, structured, slightly pompous prose that characterizes GPT outputs. It's just how I think. I've spent years developing strategies for Smart Brevity™, especially as a professor and ML engineer where time is sacred. That's precisely why I include a TL;DR with copy-pastable solutions upfront, then proceed to explain in depth. (Too many years teaching...)

Part 2: The Social Experiment

After being flagged for sounding like an AI, I decided to run a little experiment.

Next question I answered? I wrote it completely myself, then fed it to an LLM with one simple instruction:

"Rewrite this text as if you were [LLM]."

I asked the AI to rewrite everything I'd written, but in its own voice.

A single tear rolled down my cheek as I read the output. The flow was superb. The transitions were buttery smooth. I pasted it verbatim—didn't even remove the telltale characteristics I (as a Machine Learning specialist) know exist in AI-generated text.

The result? Not flagged. Better yet: the same moderator who flagged my original answer upvoted this one.

This moment crystallized something for me. We're missing a massive opportunity here. People are so focused on detecting AI that they've forgotten to ask: If the answer solves the problem, does it matter who—or what—wrote it?

But that's a story for another day.

Part 3: The Linguistic Fingerprints

What I can share today are the patterns—those statistically significant trends that betray AI authorship. These aren't smoking guns individually, but part of a larger textual fingerprint. (Actual detection involves analyzing diacritics, BOMs, and ventures into Kolmogorov Complexity theory of randomness—my favorite subject, coming in a dedicated series.)

For now, here's what to watch for:

The Rule of Three

Remember when I listed three Stack Overflow rules at the beginning? That wasn't accidental. Our brains naturally gravitate toward triplets—two feels incomplete, four feels excessive (and we're lazy). We rarely reach five.

LLMs have this obsession amplified to eleven. Three examples. Three benefits. Three challenges. Always three.

LinkedIn Talk: The Art of Saying Nothing Beautifully

You know these sentences: packed with words, empty of substance.

"The outstanding performance of the data shows an increasing demand for more sophisticated solutions among users, not only because they value safety and freedom, but also because such are intrinsic components embedded in human nature and philosophy."

What did that actually say? Not much. But it sounded profound.

Watch for the "not X, rather Y" construction. "It's not just about speed, it's about efficiency." AI loves this false dichotomy.

The Vocabulary Hall of Fame

Certain words appear with suspicious frequency in AI text:

The Classics: Additionally (especially starting sentences), crucial, delve (pre-2025), emphasizing, enhance, fostering, garner, highlight (as verb), intricate/intricacies, landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore (as verb), vibrant.

The Significance Merchants: "stands/serves as," "is a testament/reminder," "plays a vital/crucial/pivotal role," "underscores its importance," "reflects broader trends," "marks a turning point," "evolving landscape."

The Humble Helpers: "I hope this helps," "Of course!," "Certainly!," "You're absolutely right!," "Would you like...," "Let me know if you need more detail."

Structural Tells

AI writing has a typography signature:

Smart quotes: "this" or 'this' (not "this" or 'this')
Em dashes—always, ALWAYS em dashes—used liberally
Ellipsis as Unicode: … (not ...)
Emojis and bullet points everywhere 🎯
Title Case Headers
Overuse of boldface for emphasis
Two-column tables when prose would suffice

The Copula Conspiracy

LLMs avoid basic "is/are" constructions like vampires avoid garlic.

Instead: "serves as," "stands as," "marks," "represents," "boasts," "features," "offers."

Why write "The library is popular" when you can write "The library boasts a vibrant community"?

Elegant Variation Gone Wild

LLMs have repetition-penalty code (temperature parameters) that discourages word reuse. Mention a protagonist once by name, then watch the synonyms parade: "the key player," "our eponymous character," "the central figure."

It's the literary equivalent of using a thesaurus on every noun.

The Balanced Parallelism

"Not only X, but also Y." "It's not just about A, it's about B." "Despite challenges, however..."

These parallel constructions make AI sound thoughtful and balanced. They're everywhere.

Knowledge-Cutoff Disclaimers

"As of my last training update..." "While specific details are limited..." "Based on available information..." "Up to [date]..."

Humans rarely qualify their knowledge this way unless we're academics covering our asses.

Part 4: The Philosophical Epilogue

Here's what keeps me up at night: I write like an AI because my brain works differently. I structure thoughts in neat hierarchies. I use formal vocabulary. I love em dashes and parallel constructions.

Am I less human because my prose matches a statistical model trained on billions of human texts?

Or perhaps—and here's the uncomfortable truth—LLMs write like "AI" because they learned from people like me. The verbose professors. The technical writers. The documentation specialists who've been producing "AI-sounding" content since before transformers were invented.

We built these models on human language. They reflect patterns that already existed. The call is coming from inside the house.

Part 5: Conclusion

The Stack Overflow incident taught me something valuable: We're developing detection mechanisms for a problem we've barely defined. "AI-generated text" isn't a monolith—it's a spectrum that overlaps considerably with neurodivergent communication styles, academic writing, technical documentation, and yes, anyone who simply gives a damn about structure and clarity.

Maybe instead of playing an endless game of cat-and-mouse with detection algorithms, we should ask better questions:

Is the information accurate?
Does it solve the problem?
Is it helpful to the reader?

Because here's the kicker: The moderator who flagged my human-written answer as AI and upvoted my AI-rewritten answer wasn't evaluating truth or utility. He was evaluating vibes.

And vibes, my friends, are a terrible basis for gatekeeping knowledge.

So next time you're tempted to flag something as "AI-generated," pause. Ask yourself: Are you detecting artificial intelligence, or just intelligence that's been artificially forced into a box labeled "normal"?

The answer might surprise you.

P.S. This entire article was written by a human. Probably. Does it matter?

From Academia to Industry: The World Is Bigger Than You Thought

Eduardo Gade Gusmao — Sun, 18 Jan 2026 15:11:15 +0000

Part I: if all(academia): continue

I spent most of my life breathing academia.

I did my BSc, MSc, and two PhDs across Germany and the US. Research shaped how I think, how I speak, how I measure time. Deadlines, papers, grants, citations - all part of a rhythm I once thought was the rhythm of intellectual life.

After a long winter abroad, I returned to Brazil - first to teach at the same university where my academic path began, and later to join the Oswaldo Cruz Foundation, invited to work on one of the largest precision medicine initiatives in the country. Brazil has one of the world's largest & most comprehensive free public healthcare systems, and I deeply believe in that model. Open science, open access, open collaboration - those values were never abstract to me.

Then life interrupted.

Part II: driver.wait()

A serious accident left me unable to move or work for a long period. I stepped away from my position with a difficult letter - one of those moments where professionalism and humanity blur completely. There were tears. There was gratitude. And then there was silence.

With my body paused, my mind wasn't. Learning was the only thing I could still do. So I started looking outward - really outward - in a way I hadn't had time to before.

What I found surprised me.

Academia is absolutely fundamental. There is no modern AI, no genomics revolution, no computational biology without decades of academic work. That hasn't changed. What has changed is where some of the most daring experimentation now happens.

For me, "new trends" always meant mathematics/science: variational methods, stochastic optimisation, KL divergence, ELBO, reparameterization tricks - the quiet machinery behind learning systems. Or Any-seq, a novel sequencing method combining gene expression, architecture and function. But outside academia, I suddenly saw something else layered on top of that: tools that fix your code as you write, systems that assume iteration and failure as normal, environments designed to reduce friction instead of rewarding endurance.

I wasn't ignorant because I lacked curiosity. I was ignorant because academic time is brutally scarce. When survival depends on output metrics, there is little space to look out the window.

Meanwhile, industry quietly absorbed a lesson academia once owned: discovery requires room to fail. By operationalising failure - sharing it, testing it, democratising it - industry started producing research of astonishing quality. Papers like Attention Is All You Need or breakthroughs like AlphaFold weren't accidents; they emerged from cultures where experimentation was allowed to breathe.

This didn't diminish academia in my eyes. It reframed it.

Part III: from __future__ import annotations

I realised the question wasn't academia versus industry. It was whether our systems still align with the values that made discovery possible in the first place.

So I decided to transition.

Not because I stopped believing in research - quite the opposite. I still dream of ambitious endeavours, like chromatin-informed therapies and biology that genuinely changes lives. I just came to believe that the walls separating "industry" and "academia" are more historical than necessary.

Will this transition be easy? Probably not. Will my academic CV fit cleanly into industry checkboxes? Time will tell.

But this isn't about me, or about choosing sides. It's a quiet call for a different mindset - one where academia and industry learn from each other, where failure regains its dignity, and where discovery belongs to anyone willing to look carefully enough.

The world is bigger than we think - sometimes we just need the pause to notice it.