Machine Learning Engineer - Member of Technical Staff

Conductor Quantum is building quantum computers on silicon chips. Our foundational software automates the calibration and tune-up of semiconductor spin qubits - turning raw silicon hardware into a ready-to-run quantum platform. By eliminating the biggest bottleneck in quantum computing (manual device configuration), we’re enabling a few, and eventually billions, of qubits to operate reliably at scale.

This is your opportunity to do the best work of your life.

You will have a chance to shape the future of computing as part of a very small, passionate and highly technical team. A team, made up of quantum physicists, hardware engineers, and machine learning experts.

We are looking for high intelligence, high energy, and high integrity individuals who are hungry to change the world.

Role Overview: Machine Learning Engineer

As a Machine Learning Engineer, you will shape Conductor’s core offerings—software systems that analyze quantum device data in real-time, optimize qubit parameters, and seamlessly integrate with both on-premise and cloud infrastructures. You will architect, build, and maintain the next-generation control and calibration stack while collaborating closely with customers, physicists, and hardware engineers. This is a high-impact, high-ownership role, where you will define best practices, influence product strategy, and help lay the foundation for Conductor’s culture and growth.

Responsibilities and Skills

Machine Learning for Quantum Control: Design and implement Python-based pipelines to analyze complex quantum device data and automate calibration. Build machine learning models that enable real-time optimization, adaptive tuning, and intelligent control. Apply techniques like Bayesian optimization and reinforcement learning to develop systems that self-correct and scale—forming the intelligence layer of the quantum stack.

Computer Vision & Data Interpretation: Develop computer vision models to extract insights from high-dimensional experimental data, including visual and spatiotemporal signals. Apply deep learning for pattern recognition, anomaly detection, and feature extraction to support automated decision-making in quantum device workflows.

Cloud Infrastructure & Scalable Deployment: Deploy ML and CV models to the cloud (AWS, GCP, or Azure). Build backend services for model inference, data streaming, and orchestration. Ensure secure and scalable integration with lab infrastructure, hardware control systems, and external APIs.

Customer Collaboration & Product Strategy: Engage with top-tier quantum hardware teams and research labs. Translate customer challenges into ML/CV-enabled solutions, shape product features, and validate approaches through prototypes, demos, and rapid iteration.

Other Perks

• Free healthcare, dental and vision.