Principal Machine Learning Engineer

iHerb is an online health and wellness retailer focused on making healthy and balanced living accessible through innovation and an improved online shopping experience. The Principal Machine Learning Engineer will create scalable machine learning systems and platforms, develop infrastructure and abstractions for production models, and collaborate with data scientists, applied scientists, software developers, and technical teams to deliver business-impacting solutions.

Responsibilities:

• 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

Requirements:

• 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 accuracy and attention to detail
• Excellent organization skills and ability to multitask
• Experience with Microsoft Office Suite (Word, Excel, PowerPoint)
• 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
• Able to identify, troubleshoot and resolve problems quickly using sound judgment, poise and diplomacy
• Ability to use judgment and reasoning skills, and determine when to escalate issues, as required, in a timely manner
• The Team Member is regularly required to talk and hear
• The Team Member is frequently required to sit, walk, climb stairs, use hands and fingers, bend, stoop and reach with hands and arms
• Reaching above shoulder heights, below the waist or lifting as required to file documents or store materials throughout the work day
• The Team Member may occasionally lift or move office products and supplies up to 25 pounds
• Proper lifting techniques required
• Ability to work extended hours as required
• Experience with Google Business Suite (Gmail, Drive, Docs, Sheets, Forms) preferred
• Masters Degree preferred