Principal Machine Learning Engineer

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.

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

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.)

Generally requires a minimum of two (2) years relevant experience in applied machine learning or machine learning systems/infrastructure, and one (1) year of relevant work experience in machine learning engineering or related fields. (e.g., as a Machine Learning Engineer, ML Ops engineer, or related position).

Bachelor's Degree in Computer Science, Electrical Engineering, or related field required, Masters Degree preferred.