Principal Machine Learning Engineer

Job Summary: 

The Machine Learning Engineer will tackle challenging problems and create scalable machine learning systems and platforms that make an impact on millions of users. This role will work closely with business partners to provide machine intelligence driven solutions and products to simplify and enhance the customer experience and to automate core business processes. The Machine Learning Engineer will partner closely with Data Scientists, Applied Scientists, and Software Developers to ensure predictive models make business impact.

Job Expectations: 

• Partner with the Data Platform team in a two-way exchange of best practices

• Adopt common patterns and build effective abstractions across different machine learning pipelines that simplify existing machine learning processes and accelerate the modelling process from the business problem’s inception to deploying a model solution into production

• Develop horizontal solutions to robustly scale the team’s machine learning models and processes

• Build software with Object-oriented Design Patterns and Analysis (OOA and OOD) with an eye toward reducing technical debt and maintaining services at high availability

• Participate in requirements reviews, design reviews, and code reviews

• Research and prototype new technologies to support the rapid growth of the business

• Interact cross-functionally with a wide variety of technical teams and work closely with data and applied scientists to identify opportunities to improve on iHerb’s platform

The duties and responsibilities described above may provide only a partial description of this position. This is not an exhaustive list of all aspects of the job. Other duties and responsibilities not outlined in this document may be added as necessary or desirable, with or without notice.

Knowledge, Skills and Abilities:

Required:

• Strong coding experience (e.g. Java, C#, Python)

• Experience with gathering data from multiple sources using big data technologies (Spark, Hadoop, BigQuery, Athena, etc.)

• Experience building machine learning infrastructure following robust software engineering practices

• Knowledge of modern software development tools, systems, and practices (design patterns, CI/CD, git, unit testing, smoke testing, integration testing, job schedulers, cloud technologies like AWS Lambdas and Google functions, etc.)

• Exposure to all aspects of the software development life-cycle

• Experience with messaging technologies (Kafka, Google Pub/Sub, Kinesis, RabbitMQ, etc.)

• Experience with Docker and Kubernetes

• High degree of ac