Ada is an AI customer experience platform that harnesses the world's most powerful large language models to make customer service extraordinary for everyone. We’re driven to set the new standard for customer service at scale, enabling Enterprise companies to deliver experiences that are instant, proactive, personalized, and effortless.
Our team is pushing the frontier of AI Agent development, measurement, and management to power quality service experiences that respect everyone’s most valuable asset, time. Giving both customers, and customer service professionals, more quality time back for life’s big & small moments.
Since 2016, Ada is a proudly Canadian company that has powered over 4 billion interactions—automating up to 83% of all customer inquiries—for leading brands like Square, YETI, Canva, and Monday.com saving millions of hours of human effort for people all over the world.
Backed with over $250M in funding from tier-1 investors including Accel, Bessemer, FirstMark, Spark, and Version One Ventures, Ada is a pioneer in applied AI customer service.
At Ada, we see growth as a reflection of each individual owner’s personal growth. That’s why our values are rooted in driving progress and continuous improvement. If you’re ambitious and eager to grow, Ada could be the place for you.
Learn more at www.ada.cx.
As a Machine Learning Scientist within our R&D organization you will design, experiment, and deploy data science solutions that will be integrated into Ada’s product. You will work closely with Product Designers, Engineers and other ML Scientists to create delightful and impactful experiences for millions of customers.
We're looking for someone who is passionate about data science and about contributing to a world-class customer service automation platform. You will be expected to develop product features involving machine learning, and will be responsible for writing data pipelines, modelling and experimentation all the way through to deployment. You will also get the opportunity to contribute to improving the team's data science and ML engineering practices.
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