This senior-level role is central to advancing our next-generation soundtrack platform, with a focus on speech processing, style transfer, upmixing, source separation, and generative audio synthesis.

You will architect, build, and optimize cutting-edge machine learning systems at scale,leveraging foundational models, neural vocoders, latent diffusion models, and advanced retraining workflows.

As a core member of our applied R&D team, you will contribute to technical direction, collaborate across product and engineering, and deliver production-ready solutions that integrate seamlessly into creative and operational workflows for elite content creators worldwide.

What You'll Do

• Lead the research, design, and implementation of state-of-the-art machine learning algorithms for speech processing, voice transfer, source separation, and upmixing in media post-production environments.
• Drive the architecture and deployment of scalable model training pipelines using PyTorch and distributed computing frameworks.
• Develop novel generative audio models, including latent diffusion, flow-based models, variational autoencoders, and neural vocoders, optimized for professional soundtrack production.
• Own end-to-end model lifecycle management: pretraining, fine-tuning, validation, inference optimization, and CI/CD integration.
• Guide the development of personalized model adaptation workflows to support per-user tuning, cross-project continuity, and flexible deployment.
• Collaborate with product, platform, and engineering leads to define integration strategies within a secure, cloud-optimized SaaS environment.
• Stay at the forefront of generative audio, multi-modal modeling, and self-supervised learning,translating emerging research into applied innovation.
• Contribute to internal tooling and infrastructure that improves iteration speed, reproducibility, and explainability of deployed models.
• Mentor junior researchers and engineers, and contribute to a culture of rigorous experimentation, collaboration, and continuous improvement.

What We're Looking For

• MSc or PhD in Computer Science, Electrical Engineering, Applied Math, or a related field with a focus on AI/ML and multi-modal signal processing.
• 5 years of professional experience in applied ML, with a deep focus on audio-centric AI/ML research and deployment.
• Expertise in building and scaling models using PyTorch, with fluency in training, fine-tuning, and inference for deep neural networks.
• Demonstrated experience developing generative models such as VAE, GAN, diffusion models, or neural vocoders (e.g., HiFi-GAN, WaveNet).
• Deep understanding of audio-specific ML domains, including source separation, speech enhancement, music processing, and cross-modal tasks.
• Experience with MLOps tooling (e.g., Weights & Biases, MLflow, Datachain), Docker-based containerization, and scalable infrastructure for distributed training.
• Fluency in audio signal processing fundamentals and the integration of DSP into ML pipelines.
• Proven ability to contribute to architectural planning, research strategy, and production deployment in complex, multi-stakeholder environments.

Preferred Qualifications

• Familiarity with audio/text/video multi-modal frameworks and cross-domain representations.
• Experience implementing real-time or near-real-time inference pipelines in cloud or edge environments (e.g., AWS, GCP, on-prem GPUs).
• Working knowledge of latent diffusion audio models (e.g., stable-audio, AudioLDM, AudioGen).
• Strong knowledge of industry-standard audio datasets and benchmarks (LibriSpeech, VCTK, MUSDB, etc.).
• Experience optimizing inference pipelines for creative applications or interactive use.
• Proficiency in lower-level audio frameworks (C / C++, etc.)
• Contributions to published research at top-tier conferences (NeurIPS, ICASSP, ICLR, Interspeech) and/or open-source ML frameworks.

Job Data by YubHub.

As a Data Engineer (Audio/ML), you will focus on developing robust pipelines for processing complex media datasets, enabling AI/ML researchers to build transformative solutions for speech processing, style transfer, and source separation.

Responsibilities

• Design, implement, and maintain scalable, automated data pipelines for the ingestion, preprocessing, and transformation of large-scale audio datasets.
• Ensure pipelines support efficient model training and retraining workflows, enabling continuous improvement of AI/ML models.
• Collaborate with AI/ML researchers to define data requirements and integrate feedback to improve data pipeline functionality.
• Develop advanced preprocessing techniques for immersive and multichannel audio formats (e.g., Dolby Atmos, high-order ambisonics).
• Automate data cleaning, normalization, and augmentation processes to prepare datasets for various model architectures, including foundational models and transformers.
• Integrate external datasets and APIs while ensuring compliance with legal and ethical data usage standards.
• Monitor and optimize pipeline performance to handle complex and dynamic data structures effectively.
• Create tools and workflows for annotating, labeling, and curating datasets, including the use of active learning methods.
• Perform exploratory data analysis to uncover trends, validate dataset quality, and identify data gaps.

Requirements

• Master's Degree with preference for PhD in Data Engineering/Science, Computer Science, Signal Processing, or a related field.
• 8+ years of experience in data engineering or data science with a focus on building pipelines for AI/ML applications.
• Proficiency in Python, with expertise in data manipulation libraries such as Pandas, NumPy, and PyTorch's data utilities.
• Hands-on experience with audio processing libraries and tools (e.g., Librosa, FFmpeg, SoX) for handling complex audio formats.
• Familiarity with scalable pipeline tools like GitLab, Apache Spark, Airflow, or Luigi, and experience with containerized workflows (Docker, Kubernetes).
• Strong understanding of data pipeline requirements for model training, retraining, and evaluation in iterative research workflows.
• Experience with immersive and multichannel audio formats.
• Knowledge of cloud-based platforms and tools for storage and processing, such as AWS S3, Redshift, or Google BigQuery.
• Strong problem-solving skills, with a proactive mindset for addressing evolving data challenges.

Preferred Qualifications

• Experience integrating data pipelines with AI/ML workflows, including active learning and model retraining.
• Familiarity with audio-specific datasets and metadata management strategies.
• Knowledge of machine learning principles and how data quality impacts model performance.
• Experience with distributed training pipelines and large-scale dataset processing.
• Contributions to open-source projects or published research in the fields of data science or audio processing.
• Experience with visualization tools (e.g., Tableau, Matplotlib) for quality assurance and exploratory data analysis.
• Expertise in designing systems to support AI/ML model monitoring and retraining over time.

The hiring range for this position in Nicasio, CA is $173,100.00 to $232,100.00 per year.

Job Data by YubHub.

The Skysound Development Group is developing the set of next-generation tools for audio soundtracks and media distribution. We aim, through the synthesis of institutional wisdom of creative, high quality audio with cutting-edge software engineering, to bridge the divide between content creation and audience experience.

As a Senior Software Engineer within the Group, you will play a key role in conceiving and developing tools to help usher in the new era of post production audio content creation, working in areas such as application development, cloud computing, databases, and state-of-the-art security implementation. The Development Group works closely with master audio content creators to produce novel technology for immediate utilization.

Responsibilities

• Lead the software development lifecycle (design, implementation, testing, maintenance) for media pipeline components and modules
• Mentor and coach junior and mid-level engineers, fostering a culture of technical excellence
• Drive the technical roadmap and architectural decisions for major components of the media pipeline
• Implement and maintain secure code practices, utilizing security and scanning toolsets
• Diagnose and resolve complex performance and scalability issues in a cloud-native environment
• Manage multiple projects in parallel while maintaining high-quality code and attention to detail
• Research and incorporate state-of-the-art approaches to recognized challenges
• Proactively identify and address areas for improved efficiency and product quality
• Collaborate with product managers and designers to translate business requirements into technical specifications
• Work closely with team members in shared libraries, techniques and code responsibilities

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent professional experience. Master’s degree is preferred.
• 5+ years of prior experience with designing and implementing audio and video post-production workflows
• Proficiency with Go, JavaScript/TypeScript, C/C++, and Python
• Solid experience with build systems such as Docker, Makefile, CMake, and Taskfile
• Mastery of full stack development and secure programming
• Strong knowledge of CI and test automation tools (Go Test, Google Test, PyTest)
• Exceptional understanding of Kubernetes
• Expert knowledge in object-oriented programming and design patterns
• Vast experience in database design and implementation including relational and in-memory databases
• Strong familiarity with open-source and cloud-based workflows, including message brokers and serverless technologies
• API architecture expertise with REST and gRPC
• Must be detail oriented and organized, possess strong communication skills, and be able to handle a variety of tasks in an efficient manner

The hiring range for this position in San Francisco is $157,800.00 - $211,600.00 per year.

Job Data by YubHub.