Staff Software Engineer at Crusoe

dublin, Dublin County, Ireland -

Full Time

Start Date

Immediate

Expiry Date

21 Dec, 26

Salary

0.0

Posted On

22 Sep, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Industry

Information Technology & Services

Description

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.

We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.

We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.

About This Role:

Crusoe is on a mission to accelerate the abundance of energy and intelligence by building the world’s only vertically integrated AI infrastructure company from the ground up. As a Software Engineer on the Cloud Availability Platform team, you will help build Conductor—our self-driving control plane designed to predict, decide, and act autonomously to keep tens of thousands of accelerators running at peak efficiency. In this role, you will play a direct part in solving the AI compute energy bottleneck by treating power as a control input rather than a static constraint. Working alongside staff and principal engineers, you will have true ownership over greenfield services, building distributed systems that optimize power, cost, and useful compute in real time.

This is a full-time position tailored for a problem-solving engineer who thrives in ambiguity and has a strong bias toward shipping. You will tackle rare infrastructure challenges—such as energy-aware compute scheduling, closed-loop remediation, and failure prediction on noisy hardware telemetry—transforming complex operational signals into a single, programmable control plane. If you are excited to build foundational infrastructure that turns tens of thousands of accelerators across sites into one cohesive, logical system, this role offers an unprecedented opportunity to do the most meaningful work of your career.

Key Responsibilities:

Core Responsibilities: Design, build, and operate greenfield distributed services, control planes, and closed-loop remediation systems that automatically drain, checkpoint, replace, and resume live AI workloads without manual intervention.
Communication & Collaboration: Partner closely with staff and principal engineers to review architectures, establish safety rails for autonomous operations, and share technical knowledge across the cloud services team.
Daily Interactions: Collaborate daily with cross-functional partners spanning hardware engineering, energy management, data center construction, and customer support to align system design with physical infrastructure.
Key Contributions: Own the development of core platform features such as unified observability pipelines, energy-aware compute scheduling, straggler detection, and self-qualifying hardware pipelines to maximize overall system goodput.

What You’ll Bring to the Team:

Production Systems Experience: Minimum of 2+ years of experience building, shipping, and operating backend or infrastructure systems in production (e.g., distributed services, control planes, schedulers, or observability pipelines).
Distributed Systems Foundations: Demonstrated mastery of state reconciliation, retries and idempotency, consistency tradeoffs, and autonomous automation on live infrastructure.
Systems Programming Proficiency: Strong engineering fundamentals using a modern systems language such as Go, Rust, or C++.
Problem-Solving & Ownership: High comfort level working through ambiguous problem statements, proposing clear designs, and driving features from concept to production.
Shipping Mindset: A strong bias toward action and iterative delivery, favoring practical production software over over-engineered documentation.
Educational Background: Bachelor's degree or equivalent practical experience in Computer Science, Engineering, or a related technical discipline.

Bonus Points:

Hardware & HPC Familiarity: Hands-on experience or deep familiarity with GPU health telemetry, NVLink/InfiniBand/RoCE networking fabrics, or hardware thermal and power behavior.
Observability Architecture: Prior experience designing scalable observability and telemetry pipelines that span compute, storage, and networking layers.
Scheduler Internals: Familiarity or direct exposure to Kubernetes internals, Slurm, or other distributed orchestrator ecosystems.
Statistical Methods & ML: Experience applying statistical methods, anomaly detection, or time-series forecasting to noisy operational data for predictive maintenance.
Zero-Trust Security: Knowledge of zero-trust architectures, policy-based access systems, or automated multi-tenant audit streaming.

Responsibilities

About This Role:

Key Responsibilities:

Core Responsibilities: Design, build, and operate greenfield distributed services, control planes, and closed-loop remediation systems that automatically drain, checkpoint, replace, and resume live AI workloads without manual intervention.
Communication & Collaboration: Partner closely with staff and principal engineers to review architectures, establish safety rails for autonomous operations, and share technical knowledge across the cloud services team.
Daily Interactions: Collaborate daily with cross-functional partners spanning hardware engineering, energy management, data center construction, and customer support to align system design with physical infrastructure.
Key Contributions: Own the development of core platform features such as unified observability pipelines, energy-aware compute scheduling, straggler detection, and self-qualifying hardware pipelines to maximize overall system goodput.

What You’ll Bring to the Team:

Production Systems Experience: Minimum of 2+ years of experience building, shipping, and operating backend or infrastructure systems in production (e.g., distributed services, control planes, schedulers, or observability pipelines).
Distributed Systems Foundations: Demonstrated mastery of state reconciliation, retries and idempotency, consistency tradeoffs, and autonomous automation on live infrastructure.
Systems Programming Proficiency: Strong engineering fundamentals using a modern systems language such as Go, Rust, or C++.
Problem-Solving & Ownership: High comfort level working through ambiguous problem statements, proposing clear designs, and driving features from concept to production.
Shipping Mindset: A strong bias toward action and iterative delivery, favoring practical production software over over-engineered documentation.
Educational Background: Bachelor's degree or equivalent practical experience in Computer Science, Engineering, or a related technical discipline.

Bonus Points:

Hardware & HPC Familiarity: Hands-on experience or deep familiarity with GPU health telemetry, NVLink/InfiniBand/RoCE networking fabrics, or hardware thermal and power behavior.
Observability Architecture: Prior experience designing scalable observability and telemetry pipelines that span compute, storage, and networking layers.
Scheduler Internals: Familiarity or direct exposure to Kubernetes internals, Slurm, or other distributed orchestrator ecosystems.
Statistical Methods & ML: Experience applying statistical methods, anomaly detection, or time-series forecasting to noisy operational data for predictive maintenance.
Zero-Trust Security: Knowledge of zero-trust architectures, policy-based access systems, or automated multi-tenant audit streaming.