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About This Role
IREN is a vertically integrated AI Cloud provider, delivering large\-scale data centers and GPU clusters for AI training and inference. IREN’s platform is underpinned by its expansive portfolio of grid\-connected land and power in renewable\-rich regions across North America, Europe and APAC.
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.
As a Principal Solution Architect, you will sit at the tip of the spear for IREN's AI Cloud platform. You will partner directly with client CTOs, lead ML engineers, and infrastructure architects to design, benchmark, and optimize massively distributed training and inference clusters. You will convert complex customer requirements into optimized hardware/software configurations while creating reference architectures that set industry benchmarks.
- Basic Qualifications:
+ Experience: 8–10\+ years as a Solution Architect, Systems Engineer, or Infrastructure Architect handling enterprise cloud, HPC, or AI environments.
+ AI \& Hardware Mastery: Deep hands\-on experience with high\-density GPU nodes (NVIDIA HGX/DGX/Blackwell), Linux bare\-metal virtualization, and RDMA networking (InfiniBand/RoCE v2\).
+ Orchestration \& Storage: Proficiency with Kubernetes, container networking (CNI), and parallel file systems (Lustre, VAST, Ceph).
+ Customer\-Facing Execution: Proven ability to lead complex technical workshops, present to C\-suite executives, and resolve high\-stakes production bottlenecks.
- Preferred Qualifications:
+ Practical experience tuning NCCL communication, GPUDirect Storage (GDS), or custom InfiniBand routing fabrics.
+ Contributions to open\-source cloud\-native or AI orchestration projects (e.g., CNCF, Ray, Slurm, k0rdent).
+ B.S. or M.S. in Computer Science, Computer Engineering, or quantitative discipline.
- Primary Technical Authority: Serve as the trusted lead technical architect for tier\-1 enterprise customers migrating or scaling AI workloads on IREN’s infrastructure.
- Architectural Blueprinting: Design, document, and publish end\-to\-end reference architectures covering GPU clusters, InfiniBand/RoCE network topologies, Ceph/Lustre storage fabrics, and Kubernetes multi\-tenancy.
- PoCs \& Performance Benchmarking: Lead hands\-on proof\-of\-concept deployments, conducting cluster benchmarking (e.g., NCCL\-tests, Linpack, MLPerf, IO500\) to validate customer SLA requirements.
- Workload Optimization: Partner with client engineering teams to profile and optimize distributed training jobs (Megatron\-LM, DeepSpeed, PyTorch FSDP) and inference pipelines (vLLM, TensorRT\-LLM).
- Product Feedback Conduit: Translate customer pain points, telemetry, and edge cases into structured platform requirements for IREN’s core Product and Operations teams.
The IREN Package
At IREN, we offer a highly competitive compensation package that includes base salary, annual performance incentives, and opportunities to build long\-term wealth through equity programs. These offerings are part of our broader Total Rewards package, thoughtfully designed to support your health, well\-being, and long\-term success.
Compensation
- 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
- Medical, dental, and vision insurance coverage – 100% company paid for employees, 75% company paid coverage for dependents
- Company\-paid life and 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
Financial Well\-Being
- 401(k) retirement plan with company match
- Financial wellness tools and resources
Time Off \& Flexibility
- Paid Time Off (PTO) and paid holidays
- Flexible work arrangements
Growth \& Development
- Opportunities for advancement and internal mobility
- Training and personal development opportunities
Lifestyle \& 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. 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 .
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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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
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