Research Scientist: AI for Quantum Chemistry

Quantinuum
London
1 month ago
Applications closed

Related Jobs

View all jobs

Machine Learning Research Scientist - PhD, NLP, LLM

Data Science Data Science Data Modeler (Remote)

Senior Data Scientist

Data Scientist

Data Scientist

Senior Data Scientist

Are you ready to push the boundaries of what’s possible in quantum computing? Quantinuum is seeking 2 Research Scientists to delve into the applications of quantum computing for simulating molecules and materials at an ab initio level, crucially using modern machine learning and AI techniques to leverage the best of both of these groundbreaking technologies. This is your chance to work at the intersection of cutting-edge technology and real-world industrial challenges, where your research can lead to new discoveries in drug research, catalyst design, advanced materials, and beyond. You will work in a new and rapidly growing AI team, in collaboration with Quantinuum’s quantum chemistry team. You will also get to use Quantinuum’s state-of-the-art quantum hardware.View here!

In this role you will construct the AI models as part of a larger pipeline for solving our problems of interest. The role of AI/ML models includes initial identification of potential solutions, which then feed into the quantum algorithms part of the pipeline, and classical post-processing of the quantum-enabled solutions. As many of these projects are developed in collaboration with industrial partners, you will also assist in communications with them. You'll be supported by our team of AI research scientists and engineers and will also benefit from collaborations with teams dedicated to other aspects of quantum computing like quantum compilation, error correction and mitigation.

Key Responsibilities:

  • Developing and delivering projects related to the modelling of molecules and materials of industrial interest by applying AI techniques and quantum algorithms developed at Quantinuum, assessing their performance and investigating improvements
  • Collaborating with research scientists and engineers in the AI and Chemistry teams to implement new algorithms and software solutions
  • Providing scientific support in communications with customers
  • Keeping up to date with the literature of the field
  • Producing analysis and reports
  • Attending conferences and meetings with scientific customers and collaborators

Key Requirements:

  • Master’s Degree or Ph.D in Physics, Computer Science, Chemistry, Applied Mathematics, or related engineering field or equivalent experience
  • Experience in applied AI for scientific problems
  • Expertise in theoretical, computational or mathematical physics, chemistry, materials science or applied mathematics
  • Familiarity with quantum computing

Desirable Skills:

  • Familiarity with quantum computing concepts, or a related field such as quantum many-body physics
  • Experience in large-scale machine learning (large language models, reinforcement learning)
  • Experience leveraging high-performance computing (e.g. distributed multi-node, multi-GPU platforms)
What is in it for you?
Working alongside a highly talented team, with leading names in the quantum computing industry. We offer a highly competitive package, equity, 28 days of paid holiday (in addition to public holidays), a workplace pension, a positive approach to flexible working and enhanced parental and adoption benefits.

About Us:
Science Led, Enterprise Driven – Accelerating Quantum Computing
Quantinuum is the world’s largest integrated quantum company, pioneering powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With approximately 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.

We unite best-in-class software with high-fidelity hardware to accelerate quantum computing. With integrated full-stack technology, our world-class team is rapidly scaling quantum computing. We’re hiring the world’s best talent to make it happen. Join us!

Quantinuum recently secured $300m in funding, visit our news pages to learn more about this and other Quantinuum scientific breakthroughs and achievements:https://www.quantinuum.com/news

Please note that employment with us is subject to successfully passing our pre-employment screening checks. We are an inclusive equal opportunity employer. You will be considered without regard to age, race, creed, color, national origin, ancestry, marital status, affectional or sexual orientation, gender identity or expression, disability, nationality, sex, or veteran status.



jjRQA7TVQerBVCKwoCA8X2

PI262848565

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.