• Build core ML systems that power a proactive, long-horizon AI product. • Own work end-to-end: data preparation, training, evaluation, inference, and iteration. • Turn research ideas into working systems that run reliably in production. • Debug model failures and system issues using real production signals. • Iterate quickly: ship, measure outcomes, refine, and repeat. • Collaborate closely with research, product, and engineering to deliver real user impact. • Mentor and review work from other ML engineers through example and technical judgment. • Work under real production constraints: latency, cost, reliability, and safety