We are seeking outstanding engineers to play a pivotal role in the development of HP Personal System's forthcoming platform.
This platform is designed to support advanced customer products and services powered by machine learning and artificial intelligence.
Responsibilities:
Architects, develops and programs integrated software solutions, especially in support of the development, deployment and life cycle of machine learning models.
Applies machine learning and statistical modeling techniques to business or research problems.
Defines collection protocols and analyzes data sources; develops, trains and evaluates models; creates visualizations of data properties and model performance.
Deploys and maintains models.
Directs technical teams in achieving these objectives.
Keeps knowledge and skills current by reading state of the art research papers and blog posts from industry and research labs.
Studies new methods to understanding industry trends and emerging technologies.
Disseminates this knowledge within teams and across teams.
Serves as the technical lead to product and program managers to understand the business problem.
Works with software engineers to understand systems and craft interfaces to model for deployment.
Interacts with team collecting data to define collection protocols and assure data quality.
Codes, trains, validates and optimizes machine learning models, possibly crafting new model architectures, optimization techniques or objective functions.
Knowledge & Skills:
Fluent in one or more Machine Learning Frameworks (e.
g.
Pytorch, TensorFlow, scikit-learn, etc).
Fluent in Python and knowledge of one or more additional Programming Languages, such as C++, R, C#.
Knowledge of modern computer science including algorithms, data structures, software architecture.
Where applicable, knowledge of cloud and hybrid cloud service architectures and their impacts on development and deployment.
Able to architect new solutions that combine services, data preparation and preprocessing, machine learning models and data presentation.
Deep understanding of current machine learning algorithms, under which circumstances each is applicable and their pros and cons.
Able to compose new model architectures and objective functions.
Able to integrate multiple models into systems.
Knowledge of data processing techniques and data preprocessing requirements for the common machine learning approaches.
Knowledge of data collection techniques.
Knowledge of data augmentation approaches and their pros and cons.
Education and Experience:
Masters Degree in Computer Science, Statistics, and/or Math or equivalent, demonstrated through work experience, journal and/or conference publications or open-source projects.
8+ years experience.
About HP
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We’re in search of talented people who are inspired by big challenges, driven to learn and grow, and dedicated to making a meaningful difference.
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