Job Summary: 

As a Senior Machine Learning Engineer you will be taking a leading role in tackling challenging problems and creating scalable machine learning systems and platforms that make an impact on millions of users. The Senior Machine Learning Engineer will join a team of data scientists, machine learning engineers, and software developers who 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. This role will mentor junior members of the team to develop a strong future for Machine Learning at iHerb.

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

• Mentor junior engineers, unlocking their potential and helping them grow in their careers

Knowledge, Skills and Abilities:

• 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

Equipment Knowledge:

• Experience with Microsoft Office Suite (Word, Excel, PowerPoint)

• Experience with Google Business Suite (Gmail, Drive, Docs, Sheets, Forms) preferred

Experience Requirements:

Generally requires 5 (five) years relevant experience in applied machine learning or machine learning systems/infrastructure, and 2 (two) years of relevant work experience in machine learning engineering or related fields. (e.g., as a Machine Learning Engineer, ML Ops engineer, or related position).

Education Requirements: 

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