Ruari Molyneux

Audio Software Engineer with an MSc (Distinction) in Sound and Music Computing, research published at AES and professional experience in audio AI SaaS. Eager to apply research and engineering expertise to cutting-edge audio technology.

Education

Queen Mary University of London

Sound and Music Computing MSc - Distinction | SEP 2023 - AUG 2024

Project: An Artificial Reverberator Informed by Room Geometry and Visual Appearance | Published

Innovated a method for creating artificial room reverb using lidar mapping, computer vision, and delay-based acoustic simulation.
Listening tests showed improvement over state-of-the-art models.

Examiner Feedback:

"Aimed for publishable, novel, and impactful research—and achieved it." - Joshua D Reiss
"Imaginative engineering at its best—solving a problem almost no one else has tackled." - Mark Sandler

Relevant Modules:

Audio Programming

Developed real-time audio effects using C++ and the JUCE framework.
Grade: 98%

Music Informatics

Studied beat tracking, audio identification, chord/pitch recognition and more, practical applications in Python.

Fundamentals of DSP

Studied sampling theorem, spectrum analysis, filter design and more, practical applications in MATLAB.

Digital Instrument Design

Designed and prototyped digital musical instruments by combining music, design, and engineering methodologies.

Sheffield Hallam University

Computer Science BSc - 2:1 (69.4%) | SEP 2018 - MAY 2021

Work Experience

rejig | Founding Engineer

OCT 2024 - JUN 2025

Worked with an ex-BBC lead audio research engineer to design and implement a SaaS product for personalised media production.
Researched and developed AI-based features extraction pipelines for transcripts, music, and sound effects.
Developed an LLM-driven system that automates hours of audio/video editing tasks into seconds.
Collaborated with a UX/UI design agency to assess and refine wireframes, resulting in a more intuitive user experience.
Designed with a human-in-the-loop philosophy to ensure creative intent remained central to the production workflow
Involved in all stages of development, from initial idea to product launch.
Technologies: Typescript, Next.js, Node.js, Tailwind CSS, AWS, Generative AI, FFMPEG

Simpleweb | Software Engineer

FEB 2022 - FEB 2023

Designed and implemented tools on decentralized platforms.
Created a Slack-based colleague rewards system with internal tokens.
Developed a carbon-aware workspace optimisation tool for Bath University.
Engineered full-stack solutions using Next.js, Express.js, and Laravel.
Participated in client meetings to ensure precise project requirements.

Skills

Audio Development: C++, JUCE, Python, MATLAB

Web Development: React, Next.js, Typescript, Tailwind CSS, Node.js, SQL

Ops/Other: GIT, Docker, AWS

Publications

Molyneux, R. and Reiss. J. D., "An Artificial Reverberator Informed by Room Geometry and Visual Appearance". in Audio Engineering Soci- ety Convention 158th, Audio Engineering Society, 2025.

Personal Projects

Geometric and Visual Reverb

Synthesizing room reverb using geometry mapping, image recognition, and DSP techniques.
Technologies: Python, MATLAB.

Beat Tracking

Dynamic programming algorithm for efficient beat detection.
77% accuracy on a ~600 file dataset, evaluated in ~2 minutes.
Technologies: Python.

Audio Identification

Implementation of an industrial-strength audio search algorithm, also known as the Shazam algorithm.
80% accuracy on a 200 file dataset.
Technologies: Python.

Schroeder FDN

Feedback Delay Network and all-pass filter reverb plug-in.
Technologies: C++, JUCE.

Scattering Delay Network

Implementation of efficient room acoustic simulation.
Technologies: Python.

a_bowl_of_grain

A digital musical instrument mapping a physical grain of sound to a virtual grain in a granular synthesiser.
Technologies: MaxMSP, Arduino.

Interests

Music Production, Books, Graphic Design, Personal Software Projects, Live Music