AI Scientific / Mathematical Visualizer Manim, papers, and architecture drawn so an engineer can check it. Remote / Hybrid · Full-time or contract →

The seat sits between deep technical reading — machine learning, mathematics, how systems are actually deployed — and the work of making that reading visible: Manim, 2D and 3D motion, diagrams that hold under scrutiny, interactives when a still frame is not enough.

• Animate theorems, statistics and learning mechanics — Fourier transforms, gradient descent, network dynamics, attention — in 2D and 3D.
• Draw dense, readable architecture: cloud estates, agentic frameworks, model-context protocols, microservice pipelines.
• Read current AI and ML papers and turn the contribution into a short visual thread, a cut, or a chart that can stand next to the source.
• Generate motion from code: Manim first; Blender's Python API, Three.js, Matplotlib / Seaborn, Processing / p5.js where they fit.
• Produce stills and sequences for technical writing, documentation, LinkedIn, X, YouTube, and product launches — infographics, step sequences, cheat-sheets.
• Work with the engineers and writers on the delivery so the picture is accurate, not merely decorative.

What you will do

• Animate theorems, statistics and learning mechanics — Fourier transforms, gradient descent, network dynamics, attention — in 2D and 3D.
• Draw dense, readable architecture: cloud estates, agentic frameworks, model-context protocols, microservice pipelines.
• Read current AI and ML papers and turn the contribution into a short visual thread, a cut, or a chart that can stand next to the source.
• Generate motion from code: Manim first; Blender's Python API, Three.js, Matplotlib / Seaborn, Processing / p5.js where they fit.
• Produce stills and sequences for technical writing, documentation, LinkedIn, X, YouTube, and product launches — infographics, step sequences, cheat-sheets.
• Work with the engineers and writers on the delivery so the picture is accurate, not merely decorative.

What you bring

• Linear algebra, multivariable calculus, probability; Transformers, CNNs, diffusion; agentic workflows you can explain without a slide title doing the work.
• Code-driven graphics — Manim in particular — or WebGL / programmatic 3D (Three.js, p5.js, D3).
• Python for data and custom charts: NumPy, Pandas, Matplotlib, Seaborn, Plotly.
• Enough DevOps literacy to diagram Kubernetes, containers, and AI orchestration without inventing the topology.
• Colour, type, space and hierarchy used to reduce clutter, not to decorate it.
• Figma or Illustrator; After Effects, Blender or Premiere when the cut needs an edit, a voice, or a timed callout on code.

What you bring

• Linear algebra, multivariable calculus, probability; Transformers, CNNs, diffusion; agentic workflows you can explain without a slide title doing the work.
• Code-driven graphics — Manim in particular — or WebGL / programmatic 3D (Three.js, p5.js, D3).
• Python for data and custom charts: NumPy, Pandas, Matplotlib, Seaborn, Plotly.
• Enough DevOps literacy to diagram Kubernetes, containers, and AI orchestration without inventing the topology.
• Colour, type, space and hierarchy used to reduce clutter, not to decorate it.
• Figma or Illustrator; After Effects, Blender or Premiere when the cut needs an edit, a voice, or a timed callout on code.

Useful, not required

• A public trail of visual technical breakdowns — X, Substack, YouTube, GitHub, Medium.
• Interactive work on the web: React, WebGL, Canvas.
• LLM prompting and generative pipelines (Midjourney, ComfyUI, Flux, SDXL) used to accelerate assets, not to replace the argument.

Useful, not required

• A public trail of visual technical breakdowns — X, Substack, YouTube, GitHub, Medium.
• Interactive work on the web: React, WebGL, Canvas.
• LLM prompting and generative pipelines (Midjourney, ComfyUI, Flux, SDXL) used to accelerate assets, not to replace the argument.

What to send

• Three to five samples of technical breakdowns: mathematical animation, learning-system flow, or architecture drawn so it can be checked.
• Repositories or snippets that show programmatic generation — Manim, D3, Matplotlib, or equivalent.

What to send

• Three to five samples of technical breakdowns: mathematical animation, learning-system flow, or architecture drawn so it can be checked.
• Repositories or snippets that show programmatic generation — Manim, D3, Matplotlib, or equivalent.

The first quarter

• Review the technical material already in production and agree the visual standard with the engineers who own it.
• Build a reusable motion and diagram system for one recurring subject: models, mathematics, or architecture.
• Ship one complete technical sequence from source paper or system note to final cut, with every claim checked.