Teams at PhysicsX

Owns the full lifecycle of task-based, in-platform technical documentation and how-to guides, from scoping and research through validation, publication, release updates, testing, and maintenance. Partners with curriculum leads, subject matter experts, Product, Engineering, and GTM leadership to ensure guides accurately support machine learning and simulation workflows. Maintains documentation backlogs, validates technical content independently, and keeps guidance current with platform releases.

Architect and build a production-grade agentic AI platform for engineering workflows. Responsibilities include agent observability, deployment, sandboxing, evaluation, governance, identity, access control, durable execution, agent memory, and developer experience. The role requires designing secure, reliable AI infrastructure using Python, Kubernetes, Docker, Terraform, agentic frameworks, vector databases, and evaluation tools.

Develop and deploy deep learning and predictive models for engineering and physics applications. Responsibilities include data preprocessing, pipeline development, optimization, production deployment, simulation integration, customer collaboration, R&D, and presenting solutions. The role is hybrid in London and includes regular travel to customer sites worldwide.