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Audio Systems Engineer - Graduate
Job in
Greater London, London, Greater London, W1B, England, UK
Listed on 2026-02-24
Listing for:
Addition
Full Time
position Listed on 2026-02-24
Job specializations:
-
Software Development
Software Engineer, AI Engineer, Computer Science
Job Description & How to Apply Below
Graduate / Junior Audio Systems / Software Engineer Quick summary
This is a rare opportunity for a high‑potential graduate/junior engineer to build a deep blend of embedded‑adjacent engineering and backend/AI work, taking real responsibility for bodycam/cloud audio features. The team has found candidates tend to be either too hardware‑heavy or too software‑light—so a strong coding foundation plus genuine audio interest is key.
Competitive (dependent on degree and level of experience)Location: Oxford Circus - Hybrid
The role (what you’ll be doing / learning)- Work with embedded, backend and AI engineers to deliver audio features end‑to‑end
. - Support development of audio ML components and/or signal‑processing elements (feature extraction, denoise concepts, event detection, etc.).
- Write clean, testable software that’s ready for production—not just prototypes.
- Help evaluate audio quality and ML performance using real‑world data (noise, multiple speakers, movement, distance, device constraints).
- Learn how to make effective trade‑offs across edge vs cloud processing.
- Strong academics from a top university (e.g., Russell Group universities)
- Degree aligned to the work (e.g.,
Computer Science, Engineering, Maths, Physics
), with strong programming evidence. - Demonstrable interest in audio (projects, research, coursework), ideally touching audio ML or signal processing.
- Strong problem‑solving ability and the mindset to learn quickly in a fast‑moving product team.
- Music + technical crossover (e.g., Music Technology, acoustics, computational audio, MIR).
- Experience in an audio/media company (internship, placement, part‑time), or a research group working on audio.
- Side projects in speech/audio ML, DSP, streaming, embedded, or applied ML
.
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