Interested in this AI/ML Engineer role at IREN?
Apply Now →About This Role
Job Type: Full\-time \| Location: San Francisco, CA \| Department: AI Infrastructure Supply Chain \| Reporting to: VP, AI Infrastructure Supply Chain , \| Work Location Type: \#hybrid
IREN is a leading AI Cloud Service Provider, deliver large\-scale GPU clusters for AI training and inference. IREN's vertically integrated platform is underpinned by its expansive portfolio of grid\-connected land and data centers in renewable\-rich regions across the U.S. and Canada.
With 100% renewable energy, we build, own and operate our data centers and take pride in being at the forefront of sustainable solutions for the ever\-evolving applications of high\-performance compute. We believe that human progress is invaluable, but it should be done in the right way – responsibly, sustainably and having a positive impact on the communities we operate in.
The Sr. Director, AI Hardware Supply Chain Operations leads procurement operations for all AI hardware at IREN, translating Technical Sourcing’s category and supplier strategy into delivered equipment through supplier execution, materials management, and build readiness. Reporting to the VP, AI Infrastructure Supply Chain, this leader partners closely with Technical Sourcing, Integrated Planning, Data Center Operations, Engineering, and Finance to ensure GPU, server, and networking hardware is delivered on time, in full, and clear\-to\-build at every site, including region\-specific execution across EMEA and APAC.
- Own and lead procurement operations for all AI hardware, translating Technical Sourcing’s category and supplier strategy into delivered equipment across every data center site.
- Drive supplier delivery execution, holding suppliers accountable to production schedules, purchase orders, and delivery commitments to meet deployment timelines.
- Own supplier\-level demand forecasts, managing forecast accuracy and allocation requests across the hardware supply base in partnership with Technical Sourcing and Integrated Planning.
- Drive Clear\-to\-Build (CTB) processes at suppliers, ensuring components, sub\-assemblies, and finished systems meet readiness milestones before shipment and installation.
- Own materials management across the hardware supply chain, including inventory, buffers, and strategic stock positioning, to minimize excess and obsolescence risk while protecting supply continuity.
- Identify supply continuity risks, including component shortages, lead\-time volatility, and single\-source exposure, and drive mitigation and contingency plans in partnership with Technical Sourcing.
- Lead geo\-specific AI hardware supply chain management across EMEA and APAC, adapting execution plans to regional supplier bases, logistics, and import/export requirements.
- Build and lead a high\-performing procurement operations team, establishing best practices, KPIs, and governance cadences as the AI infrastructure business scales globally.
- Bachelor’s degree in Supply Chain Management, Operations, Engineering, Business, or a related field; MBA or advanced degree preferred.
- 15\+ years of progressive experience in procurement operations, supply chain management, or materials management, including 5\+ years in a senior leadership role, ideally at a hyperscaler, cloud provider, or AI infrastructure company.
- Deep experience managing supplier delivery execution and Clear\-to\-Build (CTB) processes for GPU, server, and networking hardware, including production readiness, inventory, and just\-in\-time delivery to data center sites.
- Proven track record building and running supplier forecast and materials management processes that ensure supply continuity, mitigate component and lead\-time risk, and support global production ramps.
- Experience managing geographically distributed hardware supply chains, including direct experience across EMEA and/or APAC supply chain operations.
- Strong executive presence and cross\-functional leadership skills, with a track record negotiating supplier commitments and presenting supply risk and continuity plans to senior leadership.
- Must be able to reliably commute to the assigned office location on scheduled in\-office days under the hybrid work arrangement.
- This role involves frequent travel to data center and supplier sites. IREN does not yet have an office in the Bay Area (a Santa Clara office is planned); until it opens, this role will work remotely, transitioning to hybrid on\-site work once the office is established.
At IREN, we offer a comprehensive, market\-competitive total rewards package designed to support employees’ well\-being, career advancement, and financial wealth. Our offerings reflect our commitment to Proceed with Purpose while rewarding high performance and long\-term growth.
Compensation
- Annual compensation range of $300,000 \- $375,000 USD. Actual compensation will be determined based on factors such as experience, qualifications, and market data for the region.
- Total Compensation package may be inclusive of short\-term and long\-term incentives.
- Relocation (as applicable and based on successful candidate circumstances)
Health \& Wellness
- 100% company paid health insurance premiums (medical, dental, and vision) for employees, 75% company paid coverage for dependents
- Company\-paid short\-term and long\-term disability insurance
- Voluntary life, critical illness, and accident coverage available
- Health Savings Accounts (HSA) – when combined with the High Deductible Health Plan
- Employee Assistance Program and wellness resources
Retirement \& Financial Wealth
- 401(k) retirement plan with company match
- Access to financial planning and legal services
Time Off \& Leave Programs
Paid Time Off (PTO) and paid holidays
Growth \& Development
- Internal skills training and advancement pathways
- Professional development to support certifications, continuing education, or role related training
Community \& Culture
- Company events and team\-building activities
We value diverse perspectives and believe that skills can be developed. If you’re passionate about this role, we want to hear from you — whether you meet every criteria or not. Your unique experiences might be exactly what we need!
IE US Operations Inc., the employing entity and proud member of the IREN group is an equal opportunity employer that is committed to creating an inclusive workplace. We are committed to evaluating qualified applicants and do not discriminate against protected characteristics under applicable legislation.
IE US Operations, Inc. “IREN” participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the United States.
By applying for this position and submitting your resume and application materials, you consent to the processing of your personal information in accordance with our Job Applicant Privacy Statement available on our website at www.iren.com.
Salary Context
This $300K-$375K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At IREN, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($337K) sits 57% above the category median. Disclosed range: $300K to $375K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
IREN AI Hiring
IREN has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $375K - $375K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.