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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.
We are hiring a Business Operations Manager to run the business of Cloud Operations and SRE, and to work 1:1 with the VP who leads it. One role, two halves that reinforce each other: you own how the org plans, reports and follows through, and you own the executive’s time, attention and follow-through as a system you design and continuously improve.
We are looking for someone who already runs their own stack of agents and automations, who treats “a human did this manually” as a bug, and who measures themselves by what moved forward — not by what got scheduled.
If you have been the person on your team quietly building the workflows everyone else asks to borrow, read on.
What This Role Actually Is
- A business operations owner. You run the planning and reporting spine of the org: goals and OKRs, the roadmap, the cross-functional programs, and the metrics that say whether Cloud Operations and SRE are healthy. You know the numbers before anyone asks for them, and you chase down the ones that do not make sense.
- A program driver. For initiatives that cross teams, you own the plan, the dependencies, the risks and the uncomfortable status update. Nobody should have to ask “what happened with that?”
- An operator. You will run the operating cadence: staff meetings, business reviews, planning cycles, offsites. You will run these end to end: agenda, pre-reads, decisions captured, owners assigned, follow-ups driven to closure.
- A force multiplier to the executive, not a filter. The executive support half of this job is a core part of it, not a legacy attachment. You will own the VP’s calendar, inbox, travel and follow-through, and you will make decisions on their behalf inside clearly defined boundaries widening those boundaries as trust compounds. You understand what good looks like for implementing an agentic stack to be the filter, and the organizational change management necessary to drive the teams to that filter.
- An org operations partner. Headcount and requisition tracking, hiring loops, onboarding that does not depend on tribal knowledge, team communications, and offsites you own outright.
- An automation builder. Every recurring task you touch is a candidate for an agent. Weekly reports, status roll-ups, meeting prep briefs, inbox triage and drafting, travel logistics, expense reconciliation, doc hygiene — you build it once, monitor it, and move on to the next thing. Your job is to work yourself out of your own to-do list, repeatedly. There is plenty more to do after all of this is automated; it is why you are a force multiplier and an operator.
- A judgment layer. Automation is cheap; taste is not. You will know which emails only the executive can answer, which meeting genuinely needs 60 minutes, which “urgent” request can wait until Thursday, and which agent output is subtly wrong and must never go out the door.
- 5 or more years in business operations, program management, chief-of-staff or senior executive support roles, including direct experience supporting a senior executive, and at least some of it in a fast-moving, high-ambiguity environment (startup, scale-up, or a team inside a larger company operating like one).
- Genuinely AI-native. You use AI assistants and agentic tools every day, not occasionally. You can describe — in specifics — a workflow you automated, what broke, and how you fixed it. Prompting is not a party trick to you; it is how you work. Bonus points if you are well beyond prompting and operating with context and graph loop engineering.
- Hands-on with agentic tooling. Personal AI agents and assistants for calendar, inbox and scheduling; general-purpose assistants (Claude, ChatGPT, Gemini) as daily drivers; and connective tissue between systems. You do not need to be an engineer. You do need to be unafraid of documentation and have a high level of taste.
- The ability to run a cadence and a program. Agendas, pre-reads, decision logs, status that is honest when it is bad. You are comfortable holding senior people to commitments they made, in writing, in front of their peers.
- Comfort with numbers. You can build a reporting roll-up, spot the metric that is lying, and explain a trend to leadership without dressing it up.
- Judgment under incomplete information, or with swinging-door outcomes. You would rather make a reversible decision now than wait for permission.
- Instinctive discretion. You will sit close to board material, compensation, org changes and unannounced strategy. This is table stakes.
Nice to have:
- You have built something small and useful with low-code tools — a script, a dashboard, a bot — because it was faster than asking.
- You have run a planning or OKR cycle for a technical org and lived with the result.
- You have been given scope beyond a traditional remit: a project, a budget, a vendor relationship, a team offsite you owned outright.
- Experience in an org where AI adoption was a stated strategy, not a pilot.
- Comfort with data infrastructure, energy, or high-growth technical businesses.
- You have opinions about which tools are overrated.
- You know how to speak clanker.
What will make you unhappy here:
- You want a stable, predictable week.
- You prefer to be told exactly what to do.
- You expect the executive-support half of this role to disappear once the operations half gets interesting. Both are the job.
- “That’s not my job” is in your vocabulary.
Business operations, programs and reporting
- Own the operating cadence for Cloud Operations & SRE from staff meetings, business reviews, planning cycles and offsites
- Drive cross-functional programs and initiatives to completion on behalf of the VP, tracking plans, dependencies, risks and status, and escalating early when something is off track.
- Build and maintain leadership reporting: KPI and SLO roll-ups, dashboards, weekly status and business review packages and keep the underlying data trustworthy.
- Run the goal setting and OKR cycle for the org: drafting, tracking and reporting against commitments.
- Coordinate invoice management for the teams, including vendor tracking, approval workflows, payment follow-up and record keeping.
Organisational and team operations
- Track headcount and open requisitions, coordinate hiring loops and candidate scheduling, and own onboarding for new team members.
- Maintain team records, distribution lists, organizational documents and shared files to ensure key information is accurate, accessible and well organised.
- Own internal team communications, all hands content and team offsites end to end.
- Support the processes and activities of the teams, applying judgment and discretion to identify operational needs, adapt to shifting priorities, and contribute directly to the effective management of the team’s business operations.
Executive support
- Manage the complex calendar of the VP, Cloud Operations & SRE, including scheduling meetings, appointments, travel arrangements (domestic and international), and speaking engagements.
- Organize and prepare for meetings by gathering documents, creating agendas, taking minutes, and coordinating logistics.
- Act as a liaison between the teams and external parties, screening calls and visitors, answering inquiries with professionalism, and directing communications as needed.
- Draft, review and send communications on behalf of the team as required.
- Manage department credit cards, ensuring sufficient funds are available and receipts are logged accordingly.
- Provide additional support to other departments, administrators or executives as needed.
- Other duties as assigned by the company.
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
- The expected base salary for this role USD $130,000 – $175,000/annum. + 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 annual incentive bonus, equity (long-term incentive).
- Relocation or Living-out-allowance / per diem (as appliable 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 $130K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $130K to $175K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
IREN AI Hiring
IREN has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $175K - $175K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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