My name is Mistergemba, and this is what it feels like to be a Black machine learning engineer in 2025.
It’s not a perfect GitHub portfolio, it about getting started or aesthetic coding photos its about putting in the work . For me, it was dropping out, drifting into film, feeling stuck, then fighting my way back into tech with curiosity, late nights, and a portfolio that had to speak for me.
MisterGemba.com documents that journey so someone who looks like me doesn’t walk it alone.
From Dropout to Machine Learning
Before ML, I was a software engineering dropout.
I loved building with code but felt overwhelmed and distracted. I stepped away from what felt like the “only door” and turned to film instead.
I started shooting, editing, learning visual storytelling. That time taught me something crucial: it’s all about connections, I grew to love the concept in stories, shots, code, algorithms. They just speak different languages but one thing brings it all together and that’s connection.
Eventually, I found my way back to tech with more clarity. I studied computer science, cloud, and machine learning while building projects like Beth a retail transformation and SortedQ an AI travel platform.
Being Black in Tech: The Numbers and Reality
The numbers: Black workers make up 7-8% of US tech workforce but only 3% of leadership. In AI, representation is even thinner.
What it feels like: You walk into rooms and count how many people look like you.
The work is already hard. But you’re also constantly asking:
- “Do I belong here?”
- “Will they think I’m the diversity hire?”
- “If I mess up, who will they decide people like me are?”
That’s imposter syndrome, and research shows it hits underrepresented groups harder. You’re dealing with academic pressure and racialized stereotypes. The lack of representation amplifies the doubt.
Being a Black ML engineer is heavy sometimes. You’re not just debugging code,you’re debugging your own sense of worth.
Portfolio Over Pedigree
I didn’t come in with an Ivy League background or perfect transcript.
What I had and still rely on is a portfolio:
- Projects solving real problems I care about
- ML experiments around travel, recommendations, creative tools
- A visible learning journey across MisterGemba.com, GitHub, and Startups
This is how many Black and underrepresented technologists force their way in: by letting projects speak louder than assumptions.
A strong portfolio becomes both proof and protection. Proof you can build. Protection against people who only believe what they see.
You’re Not Alone
Communities to join:
Code2040 – Connects Black and Latinx technologists with internships, training, networks
Blacks in Technology (BIT) – Global community with meetups, resources, visibility
Black in AI – Increases Black presence in AI research, practice, policy
We Build Black – Focuses on networking, mentorship, skill sharing
MisterGemba sits alongside them as a personal signal: “This is what it looks like from the inside. Come see. Come try.”
Getting Started in Machine Learning
Start here:
- Google’s Machine Learning Crash Course (free)
- Coursera ML courses (Andrew Ng’s classics)
- Kaggle (practice with real data)
- University AI programs (many online now)
Pair with:
- One language (Python)
- One project that matters to you
- One community (Discord, meetup, Code2040, BIT, Black in AI)
Then build, share, repeat.
Why I Keep Going
Being a Black ML engineer means you’ll sometimes be the only one. You’ll fight imposter syndrome. You’ll have days questioning everything,
But it also means:
You’re evidence that talent is everywhere, not just where the industry expects it.
You design systems with perspectives the field desperately needs.
You’re the person someone else points to and says, “If they did it, maybe I can too.”
That’s why I built MisterGemba.com. Not to pretend I’ve “made it,” but to show the path is real, messy, and worth walking.
If you’re a Black student, career switcher, or someone feeling late or out of place:There is room for you in tech.There is room for you in ML.Your story belongs here.
