What You'll Do
The workplace of the future will be powered by AI and the Cisco Webex Intelligence team is making that a reality. Two fast-moving startups (MindMeld and Accompany) were recently acquired by Cisco to disrupt and boost the industry's #1 Collaboration portfolio. We aim to build the next generation of collaboration software & devices - from digitizing the in-meeting experience, enhancing distributed team efficiency and improving all aspects of people connection.
The team develops innovative experiences by leveraging state-of-the-art machine learning, conversational AI and information retrieval techniques. One of the projects is Webex Assistant, the industry's most advanced voice-driven virtual assistant for meetings and collaboration. As a Machine Learning Engineer, you will be responsible for mentoring, leading and coaching junior members of the team and making key architectural decisions on the direction of our machine learning platform.Who You'll Work With
You'll join a small, high-caliber team of machine learning and natural language processing experts that are passionate about the future brought by AI. We move fast, take ownership and deliver results that matter. We're not just aiming to build a conversational AI; we strive to build the best conversational AI application the world has ever seen.Who You Are
You have a passion for and deep experience in artificial intelligence, machine learning and natural language processing. You thrive in an agile development process. You look forward to joining a high caliber team of experts. You're not just looking for a job, you're looking for an innovative product where you can make a large impact. You're self-driven and set high expectations for yourself. You take ownership and sweat the small stuff. You thrive on constructive feedback. You insist that facts drive decisions. You deliver results that matter.Our Minimum Requirements Are
- B.S., M.S., or Ph.D. in Computer Science or Machine Learning
- 5+ years of applied Machine Learning / NLP experience in the industry
- Solid knowledge of statistical classifier models (HMM, SVM, deep/recurrent ANN, CRF, LMT, etc.) and of best practices in attribute selection, dimensionality reduction, runtime performance optimization
- Familiarity with toolkits such as Scikit-learn, numpy, scipy, R, Weka, Matlab, NLTK, Stanford CoreNLP
- Fluency in Python or other scripting languages
- Knowledge of NLP techniques such as PoS tagging, NP chunking, shallow/deep parsing, NER
- Knowledge of IR concepts such as statistical search ranking, knowledge graphs, vectorial semantics, LSA, document clustering
- Strong communication skills
- Experience building production AI applications
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