Hayson Ko

I lead end-to-end product work across AI, ML, consumer experiences, and platform products, currently at Meta after prior roles at Apple, JUUL, and Tesla.

I love going into underowned product spaces where the user need is real, but the product direction, decision criteria, and path to market are still unclear. I help teams separate what we know from what we need to learn, turn customer insight and data into decision criteria, and focus the product on the highest-impact problem to solve.

At Meta, I lead product work across LLM-assisted commerce, contextual personalization, media metadata, ML for user context, and safety-critical wearable experiences, including the 0-to-1 release of driving detection for Meta Ray-Ban Display.

At Apple, I moved into early-stage AI product incubation, defining product requirements and tradeoffs under small form factor, compute, and thermal constraints, after driving the 0-to-1 launch of Apple’s first dual USB-C power adapter.

Previously at JUUL, I turned FDA-regulated consumer product constraints into roadmap decisions and regulated launch systems, helping teams decide which device variants, feature changes, and supply-critical updates were worth shipping while delivering $10M+ in annualized revenue protection and cost savings.

Earlier at Tesla, I built operator-facing workflow products, including a 0-to-1 mobile app for design specs at the point of use, and A/B tested internal factory platforms that helped Tesla scale Model 3 production from 6,000 to 7,500 cars per week. I also translated Supercharger production worker pain points into ergonomic workflow improvements.

My graduate research was supported by MERSEN Canada Toronto Inc. and NSERC while I completed my MS in Engineering Systems & Computing at University of Guelph. My research work specialized in genetic algorithms for complex systems optimization, studying how large decision spaces can be searched quickly when constraints, tradeoffs, and real-time decisions make exact optimization impractical.