NLP PEOPLE
Applied Research Scientist – Machine Learning
NLP PEOPLE, New York, New York, us, 10261
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. Company:
ADP Qualifications:
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. Industry
Computer Software, Financial Services, Human Resources Employment Type
Full-time Job Functions
Other Educational Level:
Ph. D. Tagged as:
Clustering ,
Industry ,
Machine Learning ,
Pattern Recognition ,
Ph. D. ,
Recommendation System ,
United States
#J-18808-Ljbffr
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. Company:
ADP Qualifications:
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. Industry
Computer Software, Financial Services, Human Resources Employment Type
Full-time Job Functions
Other Educational Level:
Ph. D. Tagged as:
Clustering ,
Industry ,
Machine Learning ,
Pattern Recognition ,
Ph. D. ,
Recommendation System ,
United States
#J-18808-Ljbffr