Machine Learning Scientist at Materials Nexus

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

03 Feb, 26

Salary

0.0

Posted On

05 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Material Science, GNN, Model Deployment, Simulation Data, Containerisation, JavaScript, Fortran, C++

Industry

Climate Technology Product Manufacturing

Description

Company Description At MatNex, we are pushing the boundaries of possibility in science and technology, starting with the materials that make them. We are a diverse team united by ambition, curiosity, and a shared mission to build a new world from the atom up. Job Description As a Machine Learning Scientist, you will join a team focused on the intersection of ML and Material Science. Our ambition is to apply ML across our materials discovery platform to make the most substantial impact possible. As the ML team grows, we are happy to consider candidates from all levels. Even if you do not think you are an exact fit for the role, but are passionate about our mission and work we’d still like to see your application! You will: Contribute to the design and implementation of state-of-the-art scalable and performant MLIPs suitable for high-throughput materials simulations, for example using equivariant GNNs Collaborate with our science team to accelerate our materials discovery pipeline using ML, more experienced candidates could also be influencing product roadmaps Interface with large volumes of simulation data (generated in-house) to build and refine foundational models Apply best practices throughout the model lifecycle, from experimentation to deployment Shape the role and take on broader responsibilities based on interest and experience Qualifications We are looking for talented and, more importantly, passionate individuals who are motivated by the application of science and innovation to achieve net-zero materials. Experience building machine-learning models to accelerate materials simulations (e.g. creating a GNN for property prediction) Experience building and deploying ML products in a team You may also have: Experience deploying models in a cloud environment Understanding of containerisation technology (e.g. Docker) Peer-reviewed publications on relevant topics Ability in JavaScript, Fortran, or C++ Additional Information Benefits of working for us: 📈 Stock Options: We value our employees and you to share in the success of the company. You will be a vested partner in our future achievements. 🌍 Shape the company's future: Joining us at this early-stage presents a unique opportunity to shape the direction of the company and have a meaningful impact as we continue to grow. 🌴 Flexible holidays: 33 days holiday/year which can be used on UK public holidays or one more convenient days for you. 🎂 Your birthday day off: Enjoy a well-deserved day off to celebrate and recharge. 💊 Healthcare: private healthcare services provided through Vitality and Medicash. ✈️Work from anywhere: Travel the world while you get your job done - see family, or simply explore a new place! 💻 Flexible work arrangements: The team work face to face on an average 3 days per week in our London office. Flexible working times can also be arranged. 📒 Continuous learning and growth: We’re pioneers in our field, so you'll be encouraged to expand your knowledge and skills in new areas too. Materials Nexus actively supports equality, diversity and inclusion and encourages applications from all sections of society. Research suggests women only apply when they meet 100% of the criteria. Our hiring data shows female applicants were 3 times more likely to proceed to first round interviews than male applicants. We encourage you to apply even if you do not feel you are an exact fit to the spec as we are excited by diversity of experience and above all passion to make a difference.

Responsibilities

As a Machine Learning Scientist, you will contribute to the design and implementation of scalable MLIPs for materials simulations. You will collaborate with the science team to accelerate the materials discovery pipeline using machine learning.