Spatial Audio Engineer

Audatic; Berlin; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

30 days vacation

Conference visits

Free meals and snacks

Hybrid work model (60% on-site)

Competitive compensation

Job summary

Audatic is seeking a Spatial Audio Engineer to help develop DNN-based hearing-aid algorithms. You will build high-fidelity spatial audio pipelines for training and evaluation, simulate rooms, render multichannel signals, and validate data quality to improve real-world performance for users.

Based in Berlin, you will collaborate with deep learning engineers in a diverse team and enjoy a hybrid work model with at least 60% on-site.

Qualifications

• Bachelor’s or Master’s in a technical field with 5+ years of relevant experience, or PhD with 2+ years in the field.
• Expertise in spatial audio (ambisonics, HRTFs, room simulation, binaural rendering).
• Strong knowledge of room-acoustic measures (RT60, DRR, early/late reverberation).
• Strong foundations in audio DSP.
• Experience with training data for DL models and data quality impact.
• Proficient in English communication.

Responsibilities

• Building and improving high-fidelity spatial audio pipelines for DNN training and evaluation.
• Creating tools to validate realistic spatial audio scenes and analyze training data impact.
• Collaborating with DL engineers to keep data diverse, plausible and aligned with models.
• Contributing to hearing-aid algorithms that use spatial cues to separate speech from noise.

Skills

Spatial audio

Python programming

DSP (audio)

Deep learning basics

DNN data pipelines

Communication in English

Spatial perception

Education

Bachelor’s or Master’s in a technical field

PhD in audio processing

Job description

At Audatic, we build systems to render speech from any sound scenario clear and free of noise using state-of-the-art deep learning technology. Our mission is to empower millions of people with hearing loss to enjoy interactions in demanding social settings like bars or restaurants.

As a part of the Sonova Group, our team developed the speech enhancement neural network for the Phonak Infinio Sphere - the world’s first hearing aid to run a deep neural network (DNN) for real-time speech enhancement on a custom-built, low-power, embedded neural accelerator. Since then, numerous clinical studies and extensive market feedback have confirmed the significant, life-changing benefits of this breakthrough technology.

Audatic is a small, supportive team that values collaboration and openness. We pair startup agility with Sonova’s scale, audiological expertise, and world-class resources. Everyone here has the chance to shape how we work, contribute their ideas and have a direct impact on the products we deliver - creating technology that can transform lives.

More about the role

We are looking for a Spatial Audio Engineer to help develop our next-generation, DNN-based hearing-aid algorithms. In this role, you will create realistic spatial simulations to understand and shape how our models behave in challenging everyday acoustic scenes, with direct impact on hearing-aid users.

Your responsibilities will include:

• Building and improving high-fidelity spatial audio pipelines for DNN training and evaluation, covering simulated room acoustics, multichannel rendering, spatial audio, audio signal processing, data engineering, and deep learning.
• Creating tools and analyses to validate realistic spatial audio scenes and understand how spatial training data affects model behavior in complex everyday acoustic environments,
• Working closely with Audatic’s deep learning engineers to keep training data diverse, physically plausible, perceptually meaningful, and aligned with our DNN models.
• Contributing to hearing-aid algorithms that use subtle spatial cues from multiple microphones to separate speech from noise, preserve acoustic-scene awareness, and improve communication in demanding real-world environments.

More about you

• A bachelor’s or master’s degree in a technical field and at least 5 years of relevant experience or a Ph.D. in the field of audio processing and at least 2 years of relevant experience.
• Expertise in spatial audio, such as ambisonics, HRTFs, room simulation, binaural rendering, or other spatialization techniques.
• Practical knowledge of room-acoustic measures such as RT60, DRR, early reflections, late reverberation, clarity, and diffuseness, and how to use them to validate simulated environments.
• Strong foundations in audio digital signal processing (DSP).
• Experience in spatial perception, including cues and concepts such as ITD, ILD, interaural coherence, localization, externalization, or related perceptual metrics.
• Strong Python proficiency and a demonstrated ability to write robust, clean, maintainable code.
• Familiarity with deep learning concepts, including training data, targets, loss functions, model evaluation, and how data quality affects model behavior.
• Experience developing DNN training data pipelines or working directly with deep learning models is a strong plus.
• Dependable communication skills with full professional proficiency in English.

More about what we offer

At Audatic, you’ll be part of a diverse, international, and passionate team that values collaboration and innovation. We provide an environment that allows you to balance professional growth with personal well-being.

• Competitive compensation package
• 30 days of paid vacation
• Conference visits and the opportunity to publish relevant discoveries
• Free daily meals, drinks, and snacks at our green and modern office in the heart of Berlin
• Regular team events and a yearly retreat
• Hybrid work model (at least 60% on-site)

Audatic is an equal opportunity employer.

We team up. We grow talent. We collaborate with people of diverse backgrounds to win with the best team in the market place. We guarantee every person equal treatment in regard to employment and opportunity for employment, regardless of a candidate’s ethnic or national origin, religion, sexual orientation or marital status, gender, genetic identity, age, disability or any other legally protected status.