Senior Machine Learning Engineer

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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

ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.
Complex production issues are monitored, debugged, and resolved with minimal disruption.
Training, inference, and data pipelines are robust, scalable, and maintainable over time.
Drives measurable improvements in ML systems based on real-world signals and user feedback.
Provides mentorship and technical guidance to peers, raising the overall ML engineering standard.
Collaborates cross-functionally to ensure ML features integrate seamlessly into products and meet business goals.

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