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About This Role
For A Better You
At iHerb, we believe that living a healthy and balanced life should be easy and accessible to everyone. As a team member, we’ll empower you to live this promise each day as you make a truly global impact in your career.
We constantly strive for innovation, while transforming and improving the online shopping experience for our customers. We believe that individually we are incredible, but when we come together, our growth is infinite. In an industry that is constantly evolving, we are on a mission to make an impact on the global market, and the individual and collaborative efforts of our people are paramount to helping us succeed.
Whether you work in one of our logistics centers, technology hubs, corporate offices or even from home, your role at iHerb will take you beyond what’s expected, turning challenge into change. If you're ready for it, we want you to join our team. Get started now.
Job Summary:
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.
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
The duties and responsibilities described above may provide only a partial description of this position. This is not an exhaustive list of all aspects of the job. Other duties and responsibilities not outlined in this document may be added as necessary or desirable, with or without notice.
Knowledge, Skills and Abilities:
Required:
- 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
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 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).
Education Requirements:
Bachelor’s Degree in Computer Science, Electrical Engineering, or related field required, Masters Degree preferred.
Judgment/Reasoning Ability: 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.
Physical Demands: The physical demands described here are representative of those that must be met by a Team Member to successfully perform the essential functions of this job. While performing the duties of this job, 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. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Work Environment: The noise in the work environment is usually moderate. Other factors are:
- Hectic, fast\-paced with multi\-level distractions
- Professional, yet casual work environment
- Office / Warehouse environment
- Ability to work extended hours as required
\#LI\-JC1
Compensation:
The expected salary range for this role is $205,000\.00 \- $230,000\.00 USD. The actual base pay offered will be determined by factors such as the candidate's relevant experience, education, geographic location, and internal equity.
If you like wild growth and working with happy, enthusiastic over\-achievers, you'll enjoy your career with us!
Staffing Agency Submission Notice
iHerb does not accept unsolicited 3rd party ("Agency") candidates. If you are an Agency, please send any requests to be considered as a supplier in our Vendor Management System to [email protected]. Do not contact iHerb employees directly. If requested to work on a role, any Agency candidates would be presented through the internal recruiting organization.
iHerb is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. iHerb provides equal employment opportunities to all applicants for employment and prohibits discrimination and harassment.
Salary Context
This $205K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At iHerb, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $205K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
iHerb AI Hiring
iHerb has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in CA, US. Compensation range: $230K - $260K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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