Technical Writer

PhysicsX is the physics AI company for industrials, building a new simulation software stack to deliver deep physics AI enablement across the engineering lifecycle. We are looking for a Technical Writer to own task-based, in-platform documentation and how-to guides. This person will be responsible for the complete guide lifecycle: identifying and scoping documentation needs, research, drafting, validation, publication, release updates, testing and maintenance. These guides will form the authoritative technical foundation for curriculum and training programmes. The Technical Writer will work closely with the curriculum lead, who owns learning architecture, exercises, assessments, trainer materials and broader adoption initiatives.

The documentation must enable technically literate users to complete appropriate machine learning and simulation workflows independently. Guides should evolve with every platform release and should be scoped from the engineering level through to clear, customer-facing guidance. Early priorities include addressing existing gaps, building foundational machine learning explainer content for technical and non-technical readers, and supporting new feature rollouts. This role owns procedural, task-based documentation; curriculum, training and enablement content will build on these guides rather than recreating or independently maintaining the same content.

Core responsibilities include scoping detailed how-to guides per topic, as directed by Product User Enablement GTM leadership; leveraging existing Product and Delivery materials to build repeatable, distributable guides; incorporating new product features as they ship; creating and publishing new guides in coordination with subject matter experts; maintaining existing guides on a regular schedule; partnering with the curriculum lead; and gaining approval from GTM leadership and relevant subject matter experts before publication.

Success will be measured by every priority workflow being supported by an accurate, tested how-to guide; guides being maintained alongside each platform release; published guides being usable by training curriculum without recreating procedural content; documentation gaps and stale content being tracked through a visible backlog; and product users being able to complete common workflows independently.

The role owns procedural, task-based documentation as the single source of truth for product guidance. The curriculum lead owns learning architecture, exercises, assessments and trainer materials. Subject matter experts help develop and review guides, particularly within Delivery, and provide sign-off alongside GTM leadership. Product and Engineering provide the core engineering and product concepts needed for clear, customer-facing guidance and feature updates. GTM leadership prioritises the required guide topics.

Essential experience includes demonstrable experience in technical writing, developer relations or documentation engineering; curriculum, training or technical documentation experience; strong proficiency with Markdown and ideally MkDocs or a similar tool; experience with MD or AsciiDoc files; version control via GitHub; docs-as-code workflows and CI/CD integration; familiarity with the Diataxis documentation framework; comfort using company documentation guidelines and agentic writing skills; and the ability to write clear, concise prose that respects readers’ time.

You should be able to assess documentation from the target reader’s perspective, enable technically literate non-specialists to complete documented workflows independently, verify technical accuracy without relying on engineers to write the content, build coherent information architectures, and coordinate across a wide range of subject matter experts, particularly within user-enabling Delivery.

Desirable experience includes MkDocs or similar tools such as Mintlify or Zensical, Markdown and Sphinx; a physical industry background such as simulation, machine learning, design or manufacturing; documenting APIs, SDKs or developer tooling; simulation or engineering workflows; scientific computing libraries such as NumPy and SciPy; understanding how AI agents consume documentation, including structured descriptions and tool schemas; and prior freelance experience in this space, including candidates who could convert to a permanent role and start with no notice period.