My title is project coordinator. I’m a contractor through Tundra Technical Solutions, stationed at Meta. That is not what I do.
What I do is study problems, design systems to solve them, and build those systems myself. On any given day I have multiple terminals open, each one a different project requiring an entirely different set of skills: data analysis, software engineering, product design. Most of that goes well beyond my job description. I do it because it needs doing and because I can. I also have a manager who recognized that before I did, and gave me the room to prove it.
The closest I’ve come to a name for it is AI systems builder. It’s not perfect, but it’s closer to the truth than any title I’ve been given. And it took a long time to get here. The path didn’t look like a path while I was on it.
I’ve always been this way. I spent over a decade cycling through careers: law, journalism, teaching, data science, restaurant management, nonprofit human rights work. As a kid, I kept discovering I was good at things without trying very hard. That sounds like a gift, but it was also a trap. I never had to push through difficulty, so I never learned how. I just moved on to the next thing that came easily.
That pattern carried a cost I didn’t talk about. Every year I watched people I’d started alongside build something: a practice, a title, a reputation, a trajectory that made sense to other people. I kept starting over. At first, people around me thought I was on my way somewhere. She’s figuring it out, she’ll land. But it took so long that I could feel their confidence fading. The question behind their eyes shifted from “where will she end up?” to “will she ever get it together?” That was painful, because I was asking myself the same thing. I had faith in myself longer than most people did, but even that was dying. I could learn so much, do so much, and still get nowhere.
The shame of being almost-good-enough at a dozen things and not able to point to a single one and say this is mine. It’s a specific kind of loneliness, being surrounded by people who know what they are and not having an answer when someone asks you the same question.
Early on, while I was still doing nonprofit work, I read David Epstein’s Range. It was the first time I’d encountered the argument that breadth wasn’t a deficiency. For a moment, I felt like maybe I wasn’t broken. But that feeling didn’t hold. Every time I changed direction after that, it still felt like failing. A book can tell you the pattern is valid. It can’t make the pattern stop hurting.
The first time I tried data science, I quit. I’d done a bootcamp, and one of the assignments, writing a KNN algorithm from scratch, destroyed me. I finished it eventually, but the experience left me feeling completely out of my league. Another thing I was almost good enough at. I went back to restaurants and bars, work I genuinely liked, and tried not to think about it too much. Then COVID shut that down too.
Somehow I ended up back in data science. I honestly don’t remember the moment I decided on a master’s program. I just know that when I came back, I wanted to be stronger. My bachelor’s was in Asian Studies. Not a single math class on the transcript. So I enrolled in community college, took the required calculus courses, aced them, and got into the Statistics Masters Program at Texas A&M University.
That was the first time I proved to myself I could go deep. Not because it came easy, but because I decided to do it anyway.
I graduated with the goal of becoming a data scientist. Around the same time, I started an AI book club and finished the deep learning specialization on Coursera. Machine learning engineering went from an interest to an obsession. And something shifted in me that had nothing to do with credentials or career planning. I stopped caring about being behind, about following a path, about what other people thought. For the first time I was just genuinely passionate, and that was enough to keep moving.
Then I landed a contract role at Meta, a job that required my weaknesses rather than my strengths. The role was data labeling analyst, reviewing code used to train Llama. I was honest in the interview: my background was in statistics and data science, not software engineering. They brought me on anyway.
The plan was to use this job as a bridge. By day I reviewed MLE code at Meta. By night I studied to become one: textbooks, research papers, Python, object-oriented programming. I ground through LeetCode, voluntarily, which is how you know something had fundamentally changed. For the first time in my life, I was choosing difficulty, and I loved it.
The two tracks fed each other. Every script I reviewed at work, I studied closely, looking at what made code robust, what made it fragile, what patterns held up under pressure. I had a knack for reading systems even when I couldn’t yet build them fluently. The breadth I’d accumulated turned out to be exactly the right lens for evaluating training data quality. I was learning as the model learned.
I didn’t stay in that lane long. I started taking the lead on different workflows, then reached for coordination, then project ownership. I just kept seeing what needed doing and doing it.
An unexpected opportunity came up: an interview for a DevOps and data engineering role. I’d been studying engineering on my own for months, but this was the first time I had to prove it in a room. I didn’t get the job. But I walked away knowing the ceiling I’d assumed was there didn’t actually exist.
If you read the earlier posts on this blog, you’ll see someone who was all in on becoming a machine learning engineer. That was real. I meant every word of it.
Then I watched the ground shift under the field I’d just fought my way into.
AI tools got good. Really good. I started building with them and realized something uncomfortable: the models already knew more than I did. I wasn’t just racing other engineers to get good enough. I was trying to outrun a machine that would always be faster, and I was starting from behind. The bottleneck would no longer be who could write the code. It would be who could see the problem, design the solution, and get people to actually use it.
That recognition felt like another pivot, and pivots still felt like failing. But this time I had something I’d never had before: enough depth to trust my own read. Statistics gave me intuition for what these models were actually doing under the hood. The MLE work gave me fluency with the ecosystem. I wasn’t guessing. I could see exactly how and why the field was shifting. That understanding is what gave me the confidence to step off the MLE track instead of doubling down on it.
I leaned into project management. When I looked back, I realized that nearly every role I’d held across that decade of wandering was management in some form: running a classroom, running a restaurant floor, coordinating a nonprofit. I hadn’t noticed the pattern because I was too busy feeling ashamed of it.
I moved into a project coordinator role, and for the first time I treated a role as a craft. A lot of that is because of my manager. She’s someone who gets things done exceptionally well on her own, which made it mean something when she chose to trust me with real responsibility instead. She mentored me, advocated for me, and gave me room to grow into work that was well beyond my title.
Then in early 2026, AI tools took another leap. Meta integrated AI across nearly every workflow. The gap between what I could imagine and what I could build essentially disappeared. So I stopped worrying about roles and titles and did what I’d always done, I solved problems. Except now, for the first time, I could also build the solutions myself.
My manager was running a large workforce and had no shortage of problems. She’d surface one, like no visibility on contractor performance across the program, and trust me to figure it out. I designed an evaluation system from scratch: real-time dashboard, clean data model, the right metrics, low enough friction that people used it without being asked. Another gap: QA work buried in Google Sheets, invisible to leadership. I built an add-on, partnered with data engineering, and connected it into Meta’s data ecosystem. The tool spread to other pillars on its own. I kept expanding it.
Full ownership, no title. And from the start, I was designing these systems with AI in mind, not as an afterthought but as part of the architecture. LLM integrations, automated pipelines, bots. It’s not about using AI to make things run faster. It’s about rethinking the entire design so that AI and humans work together in ways that help both do better work. Every decision I made, from the data model to the prompt design, drew on something from a phase I once dismissed as aimless exploration.
AI collapsed the distance between understanding a problem and building the solution. Before these tools, I would have specced those systems out for an engineering team and waited. Instead, I made a different bet: master the tools and think harder about product. I put myself in founder communities, not to start a company but to absorb the mentality. See a problem. Own it end to end. Ship it. Iterate. That bet paid off.
AI gave generalists execution speed they never had. But speed without judgment is just noise. The reason these tools work for me is that I spent years building the judgment first: the statistical thinking, the data intuition, the product instinct, the management experience. AI removed the bottleneck that kept people like me from acting on what we could already see. The depth is what made the seeing worth anything.
Breadth got me to the door. Depth gave me the foundation. AI gave me the speed to use both at once.
That’s why I call myself an AI systems builder, for now. It doesn’t fit perfectly. Maybe nothing ever will. But it’s closer to the truth than any title I’ve been given, and I’m done waiting for a label that captures all of it before I let myself claim any of it. I spent years resigned to a landscape that rewarded specialists and didn’t have a category for people like me. I’m still adjusting to the fact that it shifted in my direction.
I think back to reading Range all those years ago, how I wanted to believe it but couldn’t quite let myself. The validation I was looking for didn’t come from a book. It came from the work finally catching up to the pattern.
If you’re where I was, feeling behind, trying to pick a lane, embarrassed that nothing you do fits a clean title, here’s what I’d offer. At some point, you do have to go deeper into something. If you’ve been exploring long enough, you already know what feels right. Let yourself commit to it.
The fear is that depth closes doors. That choosing one thing means losing everything else. But that’s not how it works. Specializing is never-ending. You can always go further, and every time you do, it opens lateral doors you couldn’t have seen from the surface. Going deep into statistics opened data science. Data science opened machine learning. Machine learning opened AI systems. Each commitment revealed more than it foreclosed. There has never been a shortage of things to be curious about. If anything, depth is what finally gave my curiosity somewhere to land.