Audio AI & Digital Signal Processing Engineer

Blue Signal Search
London
1 week ago
Applications closed

Related Jobs

View all jobs

Audio AI & Digital Signal Processing Engineer

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Title:Audio AI & Digital Signal Processing Engineer


Company Overview:

Our client is an innovative leader in AI-driven audio technology, pioneering advancements in digital signal processing (DSP) and generative AI. With a cutting-edge approach to watermarking, forensic analysis, and sound synthesis, this company is shaping the future of audio authenticity and AI-powered content creation. Their rapidly growing team collaborates with some of the biggest names in the entertainment, media, and technology sectors.

The Opportunity:

We are seeking a highly skilled Audio AI & DSP Engineer to drive innovation in audio signal processing and machine learning applications. This is an opportunity to work at the forefront of AI-generated sound, audio watermarking, and forensic analysis in a fast-paced, high-impact environment. The ideal candidate will have a deep understanding of digital signal processing, machine learning, and generative AI models for audio applications.


Key Responsibilities:

  • Develop and optimize advanced algorithms for audio signal processing, including signal injection, enhancement, synthesis, restoration, and error correction.
  • Design and train generative AI models to create, process, and analyze audio content.
  • Implement and refine AI-based forensic audio analysis and watermarking techniques to ensure authenticity and attribution.
  • Collaborate with interdisciplinary teams to manage and preprocess large datasets for AI training.
  • Optimize model performance for real-time inference and scalability in production environments.
  • Stay at the cutting edge of research and technological advancements in audio AI, DSP, and machine learning.
  • Provide technical leadership and mentorship to junior engineers and research teams.


Required Qualifications:

  • Master's or Ph.D. in Computer Science, Electrical Engineering, Music Technology, or a related field.
  • Strong background in digital signal processing (DSP) and machine learning applied to audio.
  • Proficiency in programming languages such as Python, C++, and MATLAB.
  • Hands-on experience with deep learning frameworks (TensorFlow, PyTorch, etc.).
  • Understanding of real-time and embedded audio processing techniques.
  • Experience working with generative AI models for audio synthesis and transformation.
  • Strong problem-solving skills with the ability to implement robust, efficient, and scalable solutions.
  • Excellent communication and collaboration skills in cross-functional environments.


Preferred Experience:

  • Expertise in neural network architectures and generative adversarial networks (GANs) for audio applications.
  • Knowledge of secure audio processing techniques, watermarking, and cryptographic applications in AI.
  • Experience with cloud-based AI/ML infrastructure for training and deployment.
  • Background in audio engineering, acoustics, or music technology.


Why Join Us?

  • Be part of a high-impact, high-growth team driving innovation in AI-driven audio technology.
  • Collaborate with top-tier professionals in AI, audio engineering, and digital media.
  • Work on groundbreaking projects with direct applications in music, entertainment, and security.
  • Enjoy flexible remote work arrangements with opportunities for travel and collaboration.
  • Competitive compensation package, including salary, performance bonuses, and potential equity.


This is more than just an engineering role—it's an opportunity to influence the future of AI-powered audio solutions. If you're passionate about audio technology, machine learning, and creating cutting-edge solutions, we want to hear from you.


About Blue Signal:

Blue Signal is a leading executive search firm, specializing in engineering recruitment. Our engineering recruiting team has expertise placing high-performing talent in areas such as electrical, mechanical, civil, and telecom engineering. Learn more atbit.ly/46IAFRJ

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.