I'm Sergei Nevedomski. I live in Pittsburgh, PA, where I build ML systems that scale.

I've always been fascinated by how technology can turn complex problems into elegant solutions. My journey into software engineering started with a deep curiosity about systems and data — how they work, scale, and impact people's lives. That curiosity eventually pulled me toward machine learning, where I saw the potential to not only process information at scale but also to extract meaning from it in ways that drive real-world decisions.

Over the years, I've grown into the role of Principal ML Software Engineer, specializing in machine learning and high-performance data systems. I've led projects that bridge the gap between research and production, with a particular focus on fraud detection, credit risk, and cash flow models. These areas are at the heart of financial decision-making, and I've been driven by the challenge of making models that are not only accurate, but also robust, transparent, and scalable in production. Along the way, I've contributed to open-source projects and shared knowledge with the community, which keeps me connected to the fast-moving world of modern ML.

Currently, my main focus is on building smarter Named Entity Recognition (NER) models, refining ML pipelines, and creating tools that improve developer experience around data processing and model deployment. I believe that the best systems aren't just accurate — they're usable, adaptable, and a joy to build upon. That belief shapes both my professional work and my contributions to open-source.

Outside of engineering, I'm someone who values creativity and balance. I enjoy spending time with my family (dog and cats), learning continuously, building home automation, and creating things that make everyday life a little more efficient and enjoyable. What drives me is the challenge of turning ideas into reality — building systems that last, help people, and push the boundaries of what's possible with AI.