GPU Machine Learning Engineer

We’re looking for a Machine Learning Research Engineer to join an early-stage quantum computing start-up developing full-stack photonic quantum systems. This role sits at the intersection of advanced ML research and applied engineering, focusing on generative algorithms and hybrid quantum-classical models. You’ll work closely with clients and partners to show how ML and quantum technologies can solve real-world challenges.

Responsibilities:

• Design, implement, and evaluate new machine learning algorithms, particularly generative models (GANs, flow models, diffusion models) and hybrid quantum-classical neural networks.
• Partner with clients to translate practical problems onto the company’s quantum hardware and deliver solutions that demonstrate value.
• Expand and improve the company’s software platform with new algorithms, example applications, and research-driven features.
• Support scientific innovation by publishing findings and helping safeguard intellectual property.

Required Skills & Experience:

• Proven experience building and testing machine learning algorithms.
• Hands-on experience with GANs, diffusion models, or flow-based generative models.
• Strong programming skills in Python, PyTorch, and version control (Git).
• Advanced degree (Master’s or PhD) in Computer Science, Physics, or a related technical field.

Preferred Skills:

• Experience working with specialized computing hardware (HPC, NPUs, ASICs, or quantum processors).
• Multi-GPU training experience.
• Publication record in ML or AI research.
• Comfortable presenting and collaborating with clients or stakeholders.
• Familiarity with quantum computing principles.

Why Join:

This role offers the chance to work on cutting-edge ML research applied to quantum hardware, delivering impactful solutions that move from concept to real-world implementation. The position provides hybrid flexibility in London and direct exposure to pioneering quantum technology.