Who am I? I’m an AI/ML engineer with a strong foundation in statistics and a practical approach to solving hard problems. I’m drawn to systems that need both structure and flexibility, where success means translating messy data and ambiguous goals into clear, measurable outcomes. I work best as an individual contributor but collaborate closely across functions when it helps move things forward. I care about depth, clarity, and impact, not bells and whistles. What have I done? I’ve built ML systems at scale, powering ranking and search for high-traffic consumer products. My focus has been on improving relevance, modeling user behavior, and driving measurable business impact. Before transitioning into ML engineering, I spent several years in data science, designing experiments, analyzing noisy behavioral data, and influencing product decisions through sharp, principled analysis. I’ve also spent enough time wrangling real-world data to know that clean theory rarely survives contact with production. I know what it means to take something from scratch, whether it’s a model, a research paper, or a production system, and drive it through to something that delivers value. My PhD in statistics trained me to think deeply about uncertainty and signal, and I bring that same lens to the systems I build today. What do I care about? I care about precision in thinking, elegance in implementation, and results that actually matter. I’m drawn to technical problems that resist shortcuts, ranking trade-offs, ambiguous metrics, hard-to-measure objectives. I also care about team culture: honest reviews, low-ego collaboration, and creating space for people to grow through ownership, not hierarchy. What do I stay away from? Overcomplicated solutions. Performative work. Meetings that go in circles. I have little patience for shallow metrics or cargo cult engineering. I prefer clear direction, sharp feedback, and working with people who know when to sweat the details, and when to move on.