PhysicsX is a deep-tech company of scientists and engineers, developing machine learning applications to massively accelerate physics simulations and enable a new frontier of optimisation opportunities in design, engineering, and systems control.We help our customers fundamentally improve their concepts and designs, transform their engineering processes and drive operational product performance. We do this in some of the most advanced and important industries of our time – including Renewables & Sustainability, Space, Aerospace, Medical Devices, Additive Manufacturing and Energy. Our work creates positive impact for society, be it by improving the design of artificial hearts, reducing CO2 emissions from aircraft and road vehicles, or increasing the performance of wind turbines. The RoleWe’re looking for a passionate and innovative Senior Cloud Platform Engineer to join our dynamic, fast-paced team at PhysicsX. In this role, you’ll be at the forefront of our cloud engineering efforts, from defining our cloud strategy to building and maintaining modern, cloud-native services for the benefit of everyone within PhysicsX, including our product (enterprise platform), delivery, and research teams. This role is critical in enabling all of our teams to push the boundaries of what’s possible in the world of artificial intelligence and machine learning. If this sounds exciting to you, we would love to talk — even if you don’t tick all of the boxes.
What you will do
Build, maintain, and scale a self-service production Kubernetes cluster to run machine learning workloads in the cloud
Help develop and execute our cloud roadmap, in-line with industry best practices, with an emphasis on cloud agnosticism to support the deployment to private cloud and on-premise environments
Work closely with our internal users and customers to understand the technical requirements for projects and products
Continuously apply and improve our cloud engineering best practices and standards, and support colleagues in their adoption
Implement and maintain modern observability tooling to support the debugging and improvement of workloads hosted in public cloud, private cloud, and on-premise environments
What the ideal candidate looks like
A strong understanding of Kubernetes cluster administration, such as autoscaling and multitenancy within a cluster
A strong understanding of cloud security and identity and access management, such as principal of least privilege and vulnerability scanning
A good understanding of platform engineering paradigms, such as self-service infrastructure and GitOps
A good understanding of cloud providers, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform
Enthusiasm in using modern, cloud-native technologies, such as the CNCF landscape projects and products
Familiarity with one or more programming or scripting languages, such as Golang, Python, Bash, etc.
What we offer
Be part of something larger: Make an impact and meaningfully shape an early-stage company. Work on some of the most exciting and important topics there are. Do something you can be proud of
Work with a fun group of colleagues that support you, challenge you and help you grow. We come from many different backgrounds, but what we have in common is the desire to operate at the very top of our fields and solve truly challenging problems in science and engineering. If you are similarly capable, caring and driven, you'll find yourself at home here
Experience a truly flat hierarchy. Voicing your ideas is not only welcome but encouraged, especially when they challenge the status quo
Work sustainably, striking the right balance between work and personal life.
Receive a competitive compensation and equity package, in addition to plenty of perks
We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.