AI Systems Engineer

About the role

Conductor Quantum is building quantum superintelligence.

Quantum superintelligence is an AI that uses quantum computers to surpass the capabilities of humanity's greatest minds. We are closer to that inflection point than most people realise. Frontier models are approaching general intelligence, and AI can already generate quantum circuits faster than humans can write them. The question is no longer whether quantum and AI converge; it is who builds the infrastructure to make that convergence reliable, scalable, and useful.

That infrastructure starts with the hardware. The biggest bottleneck in quantum computing today is not qubit count; it is the manual configuration that keeps those qubits from being usable. Our software automates calibration and operations for spin and superconducting-qubit systems, turning raw cryogenic hardware into a stable, ready-to-run platform accessible through a simple chat interface. By solving that bottleneck, we are enabling thousands, and eventually billions, of qubits to operate reliably at scale.

The result is a quantum computer as easy to use as ChatGPT. The first billion users are coming. The majority, we believe, will be AI agents.

Why Now

Today, AI generates production code faster and more reliably than most of the world's software engineers. AI is already generating quantum circuits. It just needs reliable hardware to run them on, well-calibrated qubits to utilise, and a feedback loop tight enough to discover new algorithms autonomously.

The world's most capable AI models are being pointed at B2B SaaS. We are pointing ours at the frontier of science.

Role Overview

As a Member of Technical Staff you will shape Conductor's core offerings: AI software that controls quantum hardware. From agentic quantum algorithm discovery and circuit generation code tools, all the way down to calibration and control of low-level qubit pulses. You will architect, build, and maintain the next-generation control and calibration stack. You will also build and maintain the first natural language interface for quantum computing, suitable for both human researchers and AI agents.

Customer Engagement and Product Strategy

Work directly with world-leading quantum companies and research labs to identify needs, shape product features and roadmaps, and demonstrate capabilities through demos, documentation, and interactive applications.

Data Analysis and Machine Learning

Develop Python pipelines to analyse large device datasets. Build and deploy machine learning models for real-time qubit optimisation. Apply advanced techniques such as Bayesian optimisation and reinforcement learning to solve calibration challenges.

Quantum Engineering and Simulation

Analyse data from various quantum devices devices to extract quantum features, performance metrics, and optimisation opportunities. Develop simulation models using advanced computational techniques and quantum physics to replicate device behaviour. Integrate with control electronics such as arbitrary-waveform generators, microwave sources, and digital-to-analog converters.