About the role:

Customer engagement is one of the top areas of unrealized potential for modern AI. From enhancing agent productivity in customer service through conversational agents, customer retention, sentiment analysis, emerging issue identification, text analytics and visualization and semantic information retrieval - many of the most compelling customer engagement scenarios have recently been enabled by advances in deep learning based NLP and ASR. We apply state-of-the-art techniques to massive amounts of real-world customer engagement data in various forms of tabular data, natural language text, dialog and speech. As an ML engineer, you will use your knowledge and experience in applied machine learning, deep learning, and software engineering to enable a new class of AI-driven customer scenarios.

Responsibilities:

• Define and frame business use cases as AI problems; define techniques to be used, execution plan, experiments, taking into account business impact, chance of success, performance and economical practicality
• Provide technical leadership to more junior data scientists and ML engineers
• Work closely with program managers and software engineers to drive AI-first systems from business use case to production integration
• Define online and offline metrics and drive their integration into A/B testing system
• Design, develop and run deep learning experiments using semi-supervised and self-learning techniques, transfer learning and custom and modified architectures
• Devise and implement principled approaches to feedback cycles in deployed systems, human error, active learning, domain mismatch, concept drift, and similar concerns of industry-applied machine learning
• Contribute to our ML platform - Adapt development mode from experimental to reusable, repeatable, configurable software. Design and developed well-engineered object-oriented software in Python and other object-oriented languages

Qualifications:

• Bachelor’s degree or a higher degree in Computer Science, Statistics, Mathematics, or a related field
• Solid background in deep learning, statistical ML techniques and computer science.
• Expert in at least one of deep NLP, speech recognition or computer vision
• Solid computer science and software engineering skills; experience as a software engineer desired
• Understanding of internals of modern NLP, CV, or ASR models, and ability to customize architecture to different tasks and scenarios
• Experience in applied ML with a proven record of successful ML projects with strong individual contribution
• Desired: experience with online learning, contextual bandits, reinforcement learning and exploration-exploitation techniques
• Experience with databases and distributed systems