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Faculty Tech Stack

Applied AI services and frontier model safety for enterprises and government

Technology, Information and Internet London, England 201–500 employees Founded 2014 Privately Held
faculty.ai LinkedIn

Faculty is a London-based applied AI consultancy founded in 2014, built on a PhD-heavy engineering team shipping production systems across TensorFlow, PyTorch, and frontier models (OpenAI, Anthropic). The hiring mix—37 engineers, 12 data specialists, 5 security staff, plus dedicated AI Safety and Customer Director roles—reflects a services-and-products business scaling model evaluation and red-teaming work for high-stakes domains (CBRN, cybersecurity, NHS optimization). Active projects span decision intelligence platforms, bespoke AI solutions, and AI-powered digital twins, with documented pain points around scaling decision intelligence offerings and managing complex safety challenges in production.

Tech Stack 32 technologies

Core StackTensorFlow PyTorch OpenAI Anthropic Python AWS Docker Kubernetes NumPy Pandas TypeScript Terraform scikit-learn React Vue FastAPI Flask Django Go Rust C++ GitLab CI/CD Node.js PostgreSQL GitLab C# Java Azure GCP AWS CDK+2 more

What Faculty Is Building

◆Challenges

  • Optimising nhs
  • High-risk projects
  • Model red teaming for high-stakes missions
  • Optimising research and commercialisation of therapies
  • Scaling decision intelligence offering
  • Reducing bureaucratic backlogs
  • Complex safety challenges
  • Complex project requirements
  • Supporting rapid growth with limited it resources
  • Measuring learning effectiveness

▲Active Projects

  • Frontier decision intelligence platform
  • Frontier model evaluations
  • Ai safety red teaming
  • Red-teaming projects in high-risk domains like cbrn and cybersecurity
  • Ai programmes
  • Bespoke ai solutions
  • Ai-powered digital twins
  • Backend and edge/iot components for diverse client deliverables
  • Shipping production-ready code in python and compiled languages such as rust, c++, or go
  • Implementing robust ci/cd processes and containerisation strategies using docker and kubernetes

Hiring Activity

Accelerating70 roles · 25 in 30d

Department

Engineering
37
Data
12
Security
5
Product
3
Research
3
Defence
2
Design
2
Public
2

Seniority

Senior
27
Lead
20
Mid
10
Director
4
Manager
4
Principal
3
Intern
1
Junior
1

Notable leadership hires: Customer Director, AI Safety Director

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About Faculty

Faculty delivers applied AI services and products to mid-to-large organizations, governments, and public institutions. The company specializes in three areas: AI strategy and governance consulting for boards and leadership; design and implementation of production AI solutions (decision intelligence platforms, digital twins, backend systems for IoT and edge deployment); and red-teaming and safety evaluation work in partnership with frontier AI labs. The engineering stack emphasizes Python, compiled languages (Rust, C++, Go), containerization (Docker, Kubernetes), and cloud infrastructure (AWS, Azure, GCP). Faculty operates as a founder-led, privately held company with 201–500 employees based in London, hiring exclusively in the United Kingdom.

HeadquartersLondon, England
Company Size201–500 employees
Founded2014
Hiring MarketsUnited Kingdom

Frequently Asked Questions

What is Faculty's tech stack?

Core: Python, TensorFlow, PyTorch, OpenAI, Anthropic. Infrastructure: AWS, Azure, GCP, Docker, Kubernetes, Terraform, AWS CDK. Backend: FastAPI, Flask, Django, Node.js, PostgreSQL. Additional: NumPy, Pandas, scikit-learn, GitLab CI/CD, TypeScript, React, Vue, Go, Rust, C++, C#, Java.

What is Faculty working on?

Frontier decision intelligence platforms, frontier model evaluations, AI safety red-teaming in high-risk domains (CBRN, cybersecurity), bespoke AI solutions, AI-powered digital twins, backend and edge/IoT systems, and production-ready code shipping in Python, Rust, C++, and Go.

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How this profile is built

Faculty's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.