I come from a research background 🧪 with hands-on experience building open source software 💻. I earned my Master’s in Computer Science, where my work on trustworthy machine learning earned a Spotlight acceptance at NeurIPS 2024 (Link). You can find more about my work and projects here and on my webpage (link on the left).
🌱 I’m now looking to transition into a more engineering-focused role—ideally at an early- to mid-stage startup where I can have a tangible impact on growth. While much of my recent work has been in ML research on security and privacy, what truly drives me is engineering: building scalable systems and solving complex, ambiguous problems. That passion is what led me to study computer science and engineering in the first place, and it’s what I want to focus on in the years ahead. I value engineering cultures that foster both personal and professional growth, and I’m open to working across domains as long as the product is compelling, the team is strong and dynamic, and there’s room to learn and make an impact.
🔭 To support this shift, I’ve been diving into side projects to sharpen my systems design skills—combining reading with small-scale builds to better understand trade-offs and architectural choices. I’m especially interested in infrastructure and system-level challenges in deploying large language models (LLMs) at scale. Recently, I built an LLM cache to intercept and reuse responses for repeated or similar queries, cutting down on redundant computation and improving inference latency.