Implement and apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing (NLP), speech analytics, time series, reinforcement learning, and recommendation systems. Develop state-of-the art machine learning models to solve real-world problems.
Requirements
- At least one of the following: personalization, recommendation, speech recognition, or NLP
- designing machine learning experiments and frameworks including feature extraction, data augmentation, synthetic data generation, model pre- training, fine-tuning, performance evaluation, robustness testing, interpretability analysis and visualization
- outlining and evaluating intrinsic and extrinsic metrics for model performance aligned with business goals
- big data and scalable model training
- personalized contents, recommender systems, search, and ranking
- A/B testing experimentation and data-driven product development
- cloud-native deployment in a large scale distributed environment
Responsibilities
- Implement and apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing (NLP), speech analytics, time series, reinforcement learning, and recommendation systems.
- Research and explore new machine learning methods, attending industry-leading conferences, experimentation and participating in our knowledge sharing community.
- Develop state-of-the art machine learning models to solve real-world problems and apply them to tasks such as text classification, named entity recognition, topic modeling, machine translation, document retrieval, question answering, information retrieval, conversation agents, summarization, document clustering, text generation, optical character recognition, speech recognition, time-series predictions and/or recommendation systems.
- Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production.
- Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.
Other
- Master's degree in Applied Statistics, Mathematics, Operations Research, Optimization, Data Science, or related field of study plus 3 years (36 months) of experience in the job offered or as Applied Artificial Intelligence / Machine Learning, Machine Learning Engineer, Research Scientist, or related occupation.
- The employer will alternatively accept a PhD in Applied Statistics, Mathematics, Operations Research, Optimization, Data Science, or related field of study plus 1 year (12 months) of experience in the job offered or as Applied Artificial Intelligence / Machine Learning, Machine Learning Engineer, Research Scientist, or related occupation.
- experience in either: reinforcement Learning or Meta Learning
- developing and debugging production-quality code
- continuous integration models and unit test development
