[Hiring] Staff Software Engineer @IREN

Role Description

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.

Platform Engineering builds what the rest of IREN builds on. We own data engineering, automation, developer tooling, AI enablement, and product engineering, the systems that turn a fleet of data centers and GPU clusters into a platform our customers and our own teams can move quickly on. Much of it is being built now, for the first time, so there is no legacy stack to maintain and no established playbook to follow.

This is one of the most senior technical roles at IREN, and it is an individual contributor role. A staff engineer is not a manager without reports. The scope is the organisation rather than a single team. The problems you take on are the ones that cross several teams, that no one team can solve alone, and that will still matter in three years.

We expect you to think in years rather than sprints. Part of the job is holding a view of where the platform needs to be well beyond the current roadmap, making the case for it, and then sequencing the work so teams can move toward it while still shipping. You will write the design documents other teams build against, and you will be measured on whether they turned out to be right.

The clearest way a staff engineer creates value here is through other engineers, so we are looking for someone who coaches at the scale of the organisation rather than the team they sit with. That means:

• Design and architecture review across teams
• Pairing on the problems people are stuck on
• Writing the things that teach
• Delivering large pieces of work by growing the engineers who build them rather than building it all yourself

We expect deep expertise in at least one domain, and we expect you to see past organisational boundaries:

• Notice when two teams are solving the same problem twice
• Identify when the right fix sits outside your area
• Recognize when something should not be built at all

We look for a customer centric view throughout. In Platform Engineering, the customer is usually another team at IREN, and the test of a design is whether it makes their work easier, not whether it is elegant.

Qualifications

• 10 or more years of professional software engineering experience, several of them at senior level or beyond.
• Deep expertise in at least one domain, for example distributed systems, databases and storage, networking, data engineering, or applied AI systems, at the level where you are the person others come to.
• A record of technical influence beyond your own team, where the outcome depended on people you did not manage.
• Coaching at scale. Be ready to name engineers who grew because of you, and to describe what changed in how they work.
• Evidence of multi-year technical judgement: a direction you set, why you set it, and how it held up afterwards.
• Excellent written communication. Your design documents persuade people who were not in the room, and remain useful a year later.
• Experience owning systems in production, including operations, on-call, and incident response.
• A customer centric approach, and the discipline to check that a design makes someone's work easier rather than assuming it does.
• Comfort with ambiguity, including the judgement to say that something should not be built.

Requirements

• Nice to have:
• Experience at a company scaling quickly, where the architecture had to change as it grew.
• Experience with high volume data pipelines, streaming systems, or telemetry and time series data.
• Experience putting machine learning or LLM systems into production.
• Experience working close to physical infrastructure, where the software must account for hardware that fails.
• Open source contributions, published work, or talks in your domain.