Senior ML Engineer - Scalable AI Solutions

iHerb; California (MO); Hybrid; USD 205,000 - 230,000; Full time

Job summary

iHerb is seeking a Machine Learning Engineer to tackle challenging problems and build scalable ML systems that impact millions of users. You will partner with data scientists, applied scientists, and software engineers to deliver predictive models that drive business outcomes and automate core processes.

You will collaborate across teams to deploy ML pipelines, scale solutions, and maintain high-availability services, with emphasis on robust software practices (CI/CD, testing) and cloud

Qualifications

• Minimum two (2) years of experience in applied ML or ML systems/infrastructure.
• One (1) year of relevant work experience in ML engineering or related field.
• Bachelor’s Degree in Computer Science, Electrical Engineering, or related field; Master’s Degree preferred.

Responsibilities

• Partner with the Data Platform team to share best practices.
• Scale ML models and processes with robust software engineering.
• Build software with OO design patterns; minimize technical debt.
• Participate in requirements, design, and code reviews.
• Research and prototype new technologies to support growth.
• Collaborate with data scientists, applied scientists, and engineers to improve iHerb’s platform.
• Additional duties may be assigned as needed.

Skills

Java

Python

C#

Big Data

ML Infra

CI/CD

Git

Education

Bachelor's in CS/EE or related

Master's degree preferred

Tools

Docker

Kubernetes

Kafka

Google Pub/Sub

Kinesis

RabbitMQ

Spark

Hadoop

BigQuery

Athena

Job description

iHerb is seeking a Machine Learning Engineer to tackle challenging problems and build scalable ML systems that impact millions of users. You will partner with data scientists, applied scientists, and software engineers to deliver predictive models that drive business outcomes and automate core processes.

You will collaborate across teams to deploy ML pipelines, scale solutions, and maintain high-availability services, with emphasis on robust software practices (CI/CD, testing) and cloud