Machine Learning Engineer

Machine Learning Engineer Overview

This opportunity is with a deep-tech startup focused on machine learning for chip simulation and verification. The team is building advanced tools that change how complex integrated circuits are validated, with the aim of increasing simulation coverage and lowering the chance of tapeout problems. The role is suited to a machine learning engineer who wants to have a visible impact on silicon-related products and work on technology that is used by real semiconductor customers.

What you will do

• Develop machine learning and AI capabilities that support chip-simulation and verification workflows.
• Build new tools that improve simulation coverage and help semiconductor design teams reduce tapeout risk.
• Take ownership of ML systems across the full lifecycle, from data ingestion through training, deployment, and production operation.
• Design and maintain data pipelines for large, technically complex scientific and engineering datasets.
• Work on model training, inference, and export using modern ML frameworks and tooling.
• Apply strong software engineering practices, including testing, CI/CD, and the creation of clean, maintainable APIs.
• Write technical specifications, document interfaces clearly, and communicate effectively with customers and internal stakeholders.
• Collaborate closely with researchers and gain direct exposure to customer workflows in a fast-moving startup setting.
• Contribute to performance-critical parts of the system using C++ or CUDA where relevant to your background.
• Help deliver ML functionality that is embedded in production chip-design tools used by semiconductor clients.

What they are looking for

• A first degree in a relevant STEM subject.
• At least three years of industry experience building machine learning systems for production use.
• Strong hands-on experience with Python and either PyTorch or JAX, including training, inference, and model export.
• Experience creating data pipelines for complex scientific, engineering, or technical datasets.
• Strong software engineering fundamentals, including testing, CI/CD, and clean API design.
• Proven ability to own an ML system from data ingestion all the way through to deployment.
• Excellent communication skills, including the ability to write specifications, document APIs, and present to customers.
• Helpful but not essential: familiarity with circuit simulation or EDA tools.
• Helpful but not essential: C++ and/or CUDA experience for performance-sensitive components.
• Helpful but not essential: experience in a startup or similarly fast-paced team environment.

What is on offer

• A competitive salary package.
• A bonus.
• Meaningful equity.
• A flexible remote or hybrid working arrangement.
• Access to London, Cambridge, and Oxford bases.
• Support for learning and development.
• Close collaboration with researchers.
• Exposure to genuine customer workflows.
• The chance to see your work shipped into production semiconductor tools used by real customers.

How to apply

For further information, contact Chris Amison at IC Resources. The posting also includes an Apply Now option and allows candidates to submit details or upload a CV through the company’s application process.

Degree Requirement: Not Specified

Visa Sponsorship May be