Machine Learning Engineer

Key Skills

Facebook-Marketing, Planning and organization, Design, Sass, Python Programming, Regression Analysis, KPI, Data science techniques, Minor Ports, Tableau, R Programming, Statistics, Data Lake, Data pipelines, Common data structures, Design of Experiment, Presentation skills, Dash framework, Digital Transformation, Data Transformation

Job Description

Machine learning engineers turn models into software. they divide their time between development and maintenance of a deployed machine learning pipelines, developing new machine learning pipelines, and improving the scalability and reliability of a running machine learning modules.

Our machine learning engineers concentrate on how we can confidently deliver machine learning pipelines on a scale that is required to support DANA ecosystems. They are working tightly together with technical-minded people such as software engineers, devops engineers, and data engineers to ensure smooth integration between various systems. Some work that our machine learning engineers did include:

• Design and setup a unified and scalable machine learning architecture that can be applied to all data products
• Develop reusable machine learning pipeline components
• Brings model inference from experiment phase into production phase so that other DANA systems can consume prediction result in either batch or online process.

Who we are looking for Machine learning engineers are engineers by heart. They know that technical debts of machine learning pipelines can arise from the data ingested into models, from how training process is done, from how a model and data is persisted, from how inference is done, and most importantly they know how to safeguard against them. They know the importance of collaboration in machine learning pipeline development; therefore, they apply clean code practices to build machine learning modules that are easy to maintain, easy to test and easy to read.

Preferably, you have brought a machine learning model to production before, therefore you are familiar with tools and practices that will help with this process. There are a wide range of machine learning models to solve a wide range of problems, we would like you to value depth rather than breath. This is usually indicated by you knowing the limitation of a model, the situation in which a model works best, and the opposing situation.

The qualifications to be successful in this role usually come from three years of experience and a background in computer science, or computer engineering. However, we care more about what you delivered rather than what you learned. We also believe that no one starts any roles as an expert, therefore we value a hunger for learning, and willingness to continuously improve one's knowledge.

Our machine learning engineers make use of tools such as pytorch to build models, apache spark MLlib to preprocess data, and mlflow to develop and orchestrate pipeline. If you are familiar with another tool to get your work done, we are excited to hear about it.

You should be comfortable working with remote teams, that means having a sense of accountability in your work even with minimum supervision.