AI Engineer

Silicon Quantum Computing Pty Ltd; Sydney; Vor Ort; AUD 120.000 - 170.000; Vollzeit

Zusammenfassung

Silicon Quantum Computing Pty Ltd in Sydney is seeking an AI Engineer to join the Software team. This mid-level, full-time role focuses on AI and software engineering within a quantum computing context.

You will contribute to AI-powered quantum simulations and quantum-enhanced products, collaborating with researchers to deliver practical solutions for real-world problems in energy, telecom and finance. You will work with data pipelines, ML models, and production-grade software in a fast-paced,

Kenntnisse

AI

Data pipelines

ML models

Production software

Jobbeschreibung

AI Engineer at Silicon Quantum Computing, Sydney. A mid-level full time position in the Software team, listed under software engineering.

Silicon Quantum Computing (SQC) is at the forefront of global efforts to build the world's first commercial-scale quantum computer, while delivering quantum-enhanced AI and simulation products to customers today. Backed by over 25 years of technological excellence, SQC is a full-stack quantum computing company that leverages its proprietary manufacturing process to engineer atomic qubits in silicon with 0.13 nanometer precision. It is the most precise semiconductor manufacturing in the world, enabling systems with world-leading algorithmic fidelity, and a decisive advantage in the global quantum computing race. Our products are commercially deployed and generating revenue. Watermelon, our quantum-enhanced AI system, is delivering superior results on real-world problems across energy, telecom and finance. Quantum Twins, our simulation platform, provides unparalleled ability to model quantum systems, accelerating molecule and materials discovery. This is SQC: building the future of computing while delivering quantum impact today.

Nice to Have

• Retrieval over messy internal data: documents, wikis, tickets, instrument output
• Evaluation practice: building the harness, offline and online scoring
• Data engineering: pipelines, storage and the modelling that has to match a real-world process
• Enough frontend to give a user something usable
• Scientific, laboratory or manufacturing environments, and users whose requirements arrive as a problem rather than a specification
• Consulting, internal platform or enablement work, where the deliverable was someone else's capability