Theoretical Chemist/Materials Scientist as a Solution Engineer (f/m/x)

at  ExoMatter

We are looking for an exceptional Theoretical Chemist to join ExoMatter as a Solution Engineer. In this role, you will partner with the founders and our existing team to implement materials computations with our growing customer base.
Become part of a young deep tech and software startup based in Munich and revolutionize materials development with us. ExoMatter is a spin-off of the German Aerospace Center (DLR) and part of the UnternehmerTUM. We have a first product on the market with paying customers like Airbus and Bosch Siemens Hausgeräte and a strong upward trajectory.
Why are we doing this?
Our mission is to challenge the status quo of materials development to reduce the worldwide ecological footprint and increase innovation speed.
We need you to succeed!
Josua and Barbara, our co-founders, are on a mission to revolutionize materials development. ExoMatter needs superstars like you to help shape our exciting journey from scratch!
Do you think along these lines?
1. If you are equally convinced that only with more efficient materials, can we tackle the climate crisis
2. You are a maker and want to turn visions into reality. See your impact directly!
You love working in a dynamic team: trusting, exchanging feedback, and taking responsibility is crucial.

Tasks

• You are an avid and efficient communicator: you are skilled sharing your ideas with nonexpert audiences
• You will be responsible for computing inorganic materials using first-principles methods, especially Density Functional Theory (DFT)
• You will use our existing python-based software to deduce materials properties from these calculations
• You collaborate with scientists in academic research projects
• You will support the building of scalable materials computation infrastructure and standardization of calculations for automated high-throughput materials computations
• You manage the implementation and ensure that timelines are met
• You may also implement existing Machine Learning models to predict materials properties or use first-principle methods to predict polymer properties

Requirements

• You have a degree in chemistry, materials science or physics (ideally a PhD)
• You have excellent communication and project management skills
• You have experience in first-principles methods, especially in DFT
• You are familiar with Materials Informatics and have worked with resources such as The Materials Project, NOMAD, or OQMD before
• You know how to run commands on a Linux server and can write code in python
• You show a high degree of responsibility and ownership
• You have first work experience outside of research institutes
• You are fluent in English, German proficiency is a plus
• Startup experience is a plus
• You live in Munich or are willing to move there within 3 months

Benefits

• Competitive package of salary, (virtual) stock options, and benefits
• Attractive company culture in an early-stage startup
• Public transportation ticket for Munich and all of Germany (Deutschlandticket)
• Flexible work hours with a strong focus on work-life balance far beyond industry standard (35 h/week contract)
• Latest ThinkPads or MacBooks for personal and private use
• Full-time position, part-time work possible upon agreement
• Hybrid work with flexible arrangement between on-site work in our new Munich office (Münchner Technologiezentrum MTZ) and remote work

Start date: 1 May 2024
If you have any questions, please contact our Co-Founder Josua Vieten

• You are an avid and efficient communicator: you are skilled sharing your ideas with nonexpert audiences
• You will be responsible for computing inorganic materials using first-principles methods, especially Density Functional Theory (DFT)
• You will use our existing python-based software to deduce materials properties from these calculations
• You collaborate with scientists in academic research projects
• You will support the building of scalable materials computation infrastructure and standardization of calculations for automated high-throughput materials computations
• You manage the implementation and ensure that timelines are met
• You may also implement existing Machine Learning models to predict materials properties or use first-principle methods to predict polymer propertie