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Applied Research Scientist - Machine Learning (PhD University Grad)

 

Responsibilities

  • Design, build, and train highly scalable custom models and evaluate model performance

  • Adapt standard machine learning methods to apply to our use cases and to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)

  • Suggest, collect, and synthesize requirements and contribute to creating an effective feature roadmap

  • Help the team deploy production ready models to customers, to learn from customer feedback and make frequent model improvements

  • Contribute to the overall architecture and implementation of our ML infrastructure, data pipelines, inference engine(s), APIs, and products

  • Collaborate with our engineering and product teams to understand their machine learning related needs and to provide guidance and support where necessary

  • Participate in functional, technical, and code reviews

  • Work in an Agile environment

Minimum Qualifications

  • Currently has or is in the process of obtaining a PhD degree or completing a postdoctoral assignment in the field of Machine Learning or similar

  • Must be available to start employment on or after July 1, 2019

  • Able to obtain work authorization in the US beginning in 2019

  • Research and/or work experience in machine learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, information retrieval or computer vision

  • Experience in systems software or algorithms

  • Knowledge in Python, including experience with ML related packages such as NumPy and pandas

  • Proven track record of achieving results as demonstrated by grants, fellowships, patents, as well as first-authored publications at workshops or conferences such as ICML, NIPS, KDD or similar

  • Demonstrated software engineer experience via an internship, work experience, coding competitions, or used contributions in open source repositories (e.g. GitHub)

Preferred Qualifications

  • Experience in applying machine learning for a global-scale enterprise or consumer application

  • Demonstrated knowledge and ability working with AWS, Google Cloud, or other cloud-based solutions to train models, set up data pipelines, and set up inference engines

  • Experience in microservices, Kubernetes, Docker, or other containerizers

  • Working knowledge of Node.js, JavaScript, and related technologies and frameworks

  • Knowledge of Continuous Integration & Delivery methodologies

  • Excellent problem-solving skills especially debugging of complex software systems

  • Excellent written and verbal communication skills

  • A passion for applying latest technologies into the development of innovative features and products

  • A collaborative attitude and demonstrated team-working ability

  • Self-motivated with a strong passion for learning