AI Audio Systems Engineer

About Pocket FM

Pocket FM is the world's leading Gen AI-native audio entertainment platform. We recently crossed $400M ARR, scaling our second $200M in just 12 months by rebuilding the storytelling stack around AI.

With 250M+ listeners and 80,000+ hours of content produced monthly, we aren't just automating stories, we're supercharging them. We provide creators with the high-velocity iteration tools once reserved for engineers, enabling global, data-aware storytelling that hits harder and travels faster.

About the role

The AI Audio Systems Engineer will build the technology that enables the Sound team to work faster, more consistently, and at greater scale.

This role combines audio software engineering, digital signal processing, audio plugin development, music technology, AI automation, and workflow systems. You will work closely with Sound specialists to understand production challenges and turn them into reliable tools, intelligent workflows, and scalable audio

systems.

This is not a show post-production role. You will not be responsible for editing or mixing episodes as your primary function. Your focus will be on building the systems, tools, models, plugins, and automation that support dialogue processing, mixing, analysis, spatial audio, quality control, and future Sound workflows.

What you will build

• Audio software, DSP systems, plugins, DAW extensions, and internal tools for Sound production.
• Automated workflows for dialogue analysis, cleanup, processing, mixing, mastering, rendering, and quality control.
• AI-assisted systems that can analyse audio, identify problems, recommend processing, and support human decision-making.
• LLM-powered tools that help Sound teams search, diagnose, configure, document, and operate audio workflows.
• Evaluation systems that measure intelligibility, artefacts, loudness, consistency, processing time, and overall production quality.
• Spatial audio and immersive audio workflows where they add value to the listener experience.
• Reusable infrastructure that allows Sound tools to move from prototype to reliable production systems.

Core responsibilities

1. Audio software and DSP

• Design and develop audio processing systems for dialogue, music, ambience, effects, mixing, and mastering.
• Work with digital signal processing concepts such as filters, dynamics, noise reduction, loudness, phase, latency, resampling, and real-time audio.
• Build reliable audio pipelines for both real-time and offline processing.
• Understand how audio moves through DAWs, plugins, buses, routing, automation, monitoring, and delivery systems.

2. Plugin and music technology development

• Build, integrate, and maintain audio plugins, DAW extensions, scripts, and production utilities.
• Work with technologies such as C++, JUCE, Python, JavaScript or TypeScript, Lua, Max, or equivalent tools.
• Develop interfaces that allow Sound specialists to use complex processing without unnecessary technical friction.
• Create reusable components, presets, templates, APIs, and systems that support multiple Sound workflows.

3. AI audio and machine learning integration

• Integrate AI capabilities such as speech enhancement, source separation, diarization, transcription, voice activity detection, voice conversion, text to speech, and audio classification.
• Evaluate AI models and services for quality, latency, cost, reliability, and suitability for production use.
• Design human-in-the-loop workflows where AI accelerates repetitive work while Sound specialists retain creative and editorial control.
• Build test datasets, benchmarks, and evaluation processes for AI-assisted audio tools.

4. LLM and workflow automation

• Use LLMs as an interaction and orchestration layer around audio tools, models, metadata, and production systems.
• Build tools that can interpret Sound requests, recommend workflows, generate configurations, explain processing decisions, or trigger approved audio operations.
• Explore agentic workflows, tool calling, structured outputs, retrieval systems, and workflow automation where they improve Sound operations.
• Connect AI systems to DAWs, audio-processing services, asset pipelines, databases, APIs, and internal tools.
• Design guardrails, validation steps, permissions, logging, and human approval points for AI-driven workflows.

5. Sound platform and infrastructure

• Work with Sound specialists to identify production bottlenecks and convert them into clear technical requirements.
• Build modular systems that can be reused across dialogue processing, mixing, mastering, spatial audio, and quality control.
• Support batch processing, queues, cloud or local rendering, asset management, versioning, monitoring, and reproducible execution.
• Document system behaviour, signal flow, configuration, known limitations, and recovery procedures.
• Help move successful prototypes into stable tools that teams can use and maintain.

What success looks like

• Sound teams can access faster and more reliable tools without needing to understand the underlying engineering.
• Dialogue and audio analysis workflows become more consistent, measurable, and easier to scale.
• AI systems reduce repetitive work while preserving audio quality and human judgement.
• Prototypes become dependable tools rather than isolated experiments.
• Plugins, workflows, and models are documented, evaluated, versioned, and maintainable.
• The Sound team has a growing technology foundation for future audio production.

Required experience

• Professional experience in audio software engineering, DSP, plugin development, music technology, audio tools, or a related field.
• Strong programming experience in one or more relevant languages such as C++, Python, JavaScript, TypeScript, or Lua.
• Practical understanding of audio signal flow, DAWs, plugins, routing, gain staging, latency, phase, dynamics, and digital audio fundamentals.
• Experience building software used by creative, media, music, audio, or production teams.
• Experience integrating APIs, AI models, machine learning services, or automated processing systems.
• Ability to understand Sound-team needs and translate them into useful technical systems.
• Strong debugging, documentation, testing, and problem-solving skills.

Preferred experience

• Audio plugin development using JUCE, VST3, AU, AAX, or equivalent technologies.
• Experience with DSP, real-time audio, embedded audio, audio middleware, or media-processing systems.
• Experience with speech, voice, or audio AI.
• Experience building LLM-powered tools, agents, tool-calling workflows, RAG systems, or automation platforms.
• Experience with cloud infrastructure, containers, queues, APIs, databases, observability, or media pipelines.
• Experience with spatial audio, binaural processing, ambisonics, object-based audio, or immersive formats.
• Experience designing audio evaluation datasets, benchmarks, regression tests, or quality-control systems.

What we are looking for

We are looking for someone who can move comfortably between audio engineering, software development, AI systems, and the practical needs of an Audio Post Production team.

You should be curious about new technologies, strong enough in audio to recognise what good sounds like, and practical enough to build tools that work in real production environments.

You do not need to be an expert in every area listed above. Strong fundamentals, good judgement, and evidence of building useful systems matter most.

Portfolio or application evidence

Please include examples of:

• Audio plugins, DSP systems, DAW tools, scripts, or audio software you have built.
• AI, automation, LLM, or workflow systems you have developed or integrated.
• Audio-processing tools or systems that improved speed, quality, consistency, or scale.
• A short explanation of the problem, technical approach, evaluation method, and result.