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- Tasks: Develop and optimise algorithms for audio signal processing and generative AI models.
- Company: Join an innovative leader in AI-driven audio technology, shaping the future of sound.
- Benefits: Enjoy flexible remote work, competitive pay, performance bonuses, and potential equity.
- Why this job: Work on groundbreaking projects with top professionals in a fast-paced, high-impact environment.
- Qualifications: Master's or Ph.D. in relevant fields; strong DSP and machine learning background required.
- Other info: This role offers mentorship opportunities and the chance to influence AI-powered audio solutions.
The predicted salary is between 43200 - 72000 £ per year.
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.
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.
- 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.
- 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.
- 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.
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.
Audio AI & Digital Signal Processing Engineer employer: Blue Signal Search
Contact Detail:
Blue Signal Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Audio AI & Digital Signal Processing Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in audio AI and digital signal processing. Follow industry leaders on social media, read relevant research papers, and engage in online forums to stay updated. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Network with professionals in the audio technology sector. Attend conferences, webinars, or local meetups focused on AI and DSP. Building connections can lead to valuable insights about the company and potentially even referrals, which can significantly boost your chances of landing the job.
✨Tip Number 3
Showcase your practical experience with projects related to audio processing and machine learning. Whether it's through a personal portfolio, GitHub repository, or contributions to open-source projects, having tangible examples of your work can set you apart from other candidates.
✨Tip Number 4
Prepare for technical interviews by brushing up on your programming skills, particularly in Python, C++, and MATLAB. Practice solving problems related to DSP and machine learning algorithms, as this will help you feel more confident and ready to tackle any technical challenges during the interview process.
We think you need these skills to ace Audio AI & Digital Signal Processing Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in digital signal processing and machine learning. Include specific projects or roles that demonstrate your proficiency in audio technology and programming languages like Python, C++, and MATLAB.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for audio technology and AI. Mention any relevant experience with generative AI models and your understanding of real-time audio processing techniques. Be sure to explain why you want to work with this innovative company.
Showcase Relevant Projects: If you have worked on projects involving audio synthesis, forensic analysis, or watermarking, be sure to include these in your application. Provide links to any relevant work or publications that can demonstrate your expertise in the field.
Highlight Collaboration Skills: Since the role involves working with interdisciplinary teams, emphasise your communication and collaboration skills. Share examples of how you've successfully worked in cross-functional environments to achieve project goals.
How to prepare for a job interview at Blue Signal Search
✨Showcase Your Technical Skills
Be prepared to discuss your experience with digital signal processing and machine learning in detail. Bring examples of algorithms you've developed or optimised, and be ready to explain the technical challenges you faced and how you overcame them.
✨Demonstrate Your Knowledge of AI Models
Since the role involves generative AI models, make sure you can talk about your hands-on experience with frameworks like TensorFlow or PyTorch. Discuss specific projects where you've implemented these technologies and the impact they had on audio applications.
✨Prepare for Problem-Solving Questions
Expect to face technical problem-solving scenarios during the interview. Brush up on your ability to think critically and articulate your thought process clearly. Practice explaining how you would approach real-time audio processing challenges.
✨Highlight Collaboration Experience
This role requires working with interdisciplinary teams, so be ready to share examples of successful collaborations. Discuss how you’ve communicated complex technical concepts to non-technical team members and how you’ve contributed to team success.
