Machine Learning Engineer-SD
3 weeks ago
Responsibilities
• Build and improve machine learning and analytics platform.
o Apply cutting edge technologies and tool chain in big data and machine
learning to build machine learning and analytics platform.
o Keep innovating and optimizing the machine learning workflow, from data
exploration, model experimentation/prototyping to production.
o Provide engineering solution and framework to support machine learning and
data-driven business activities at large scale.
o Perform R&D on new technologies and solutions to improve accessibility,
scalability, efficiency and us abilities of machine learning and analytics
platform.
• Work with data scientists to build end-to-end machine learning and analytics solution
to solve business challenges.
o Turn advanced machine learning models created by data scientists into endto-end production grade system.
o Build analytics platform components to support data collection, exploratory,
and integration from various sources being data API, RDBMS, or big data
platform.
o Optimize efficiency of machine learning algorithm by applying state-of-the-art
technologies, i.e. distributed computing, concurrent programming, or GPU
parallel computing.
• Establish, apply and maintain best practices and principles of machine learning
engineering.
o Study and evaluate the state of the art technologies, tools, and frameworks of
machine learning engineering.
o Contribute in creation of blueprint and reference architecture for various
machine learning use cases.
o Support the organization in transformation towards a data driven business
culture.
Work Relationships
• Internal
o Work closely with data scientists, business team, and project managers to
provide machine learning and data-driven business solution.
o Collaborate with other technology teams to build platform and framework to
enable machine learning and data analytics activities at large scale
• External
o Maintain engineering principles and best practices of machine learning
framework and technologies.
Requirements
• PhD/Masters/Bachelors in Computer Science, Computer Engineering, Statistics,
Applied Mathematics, or related disciplines.
• 5+ years of experience in software engineering or DevOps automation or data
engineering
• Excellent understanding of software engineering principles and design patterns.
• Excellent programming skills in either Python or Java.
• Hands-on experience in containerization/ virtualization platforms, e.g.
Docker/Kubernetes.
• Exposure to data science and machine learning technologies and methodologies.
• Good working knowledge of high performance computing, parallel data processing,
and big data stack, e.g. Spark and Hadoop/Yarn.
• Experience to one or more commercial / open source data warehouses or data
analytics systems, e.g. Teradata, is a big plus.
• Experience to one or more NoSQL databases is a big plus.
• Experience or Cloudera Data Science Workbench, is a big plus.
• Passion about machine learning and data-driven intelligence system.
• Excellent communication and presentation skills in English.
• Team player, self-starter, ability to work on multiple projects in parallel is necessary.
• Experience working in multi-cultural environments
Tell employers what skills you have
Machine Learning
Scalability
Big Data
Software Engineering
Open Source
Data Engineering
Python
High Performance Computing
Statistics
Data Science
Java
API
Data Analytics
Parallel Computing
Databases
Applied Mathematics
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