Principal Machine Learning Engineer

🕒 July 10

🏄 California – Remote

🏄 California – Remote

💵 $205k - $230k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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iHerb

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🛍️ eCommerce

🛒 Retail

🧘 Wellness

eCommerce • Retail • Wellness

iHerb is an online retailer founded in 1996 that provides a curated selection of health and wellness products to consumers worldwide. The company focuses on vitamins, natural supplements and remedies, sports nutrition, natural and dry foods, and environmentally friendly goods, with a mission to make health and wellness accessible, affordable, and convenient. iHerb operates as an e-commerce business with a retail focus and emphasizes customer experience and a values-driven company culture.

📋 Description

• Tackle challenging problems and create scalable machine learning systems and platforms that make an impact on millions of users • 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 • 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) • 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 various technical teams and work closely with data and applied scientists

🎯 Requirements

• 2+ years of relevant experience in applied machine learning or machine learning systems/infrastructure • 1+ years of relevant work experience in machine learning engineering or related fields • 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

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible working hours • Professional development opportunities