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Audio Machine Learning Jobs in Seattle, WA (NOW HIRING)

DevSelect

Principal Data Scientist

Redmond, WA · On-site

Understanding of machine learning techniques including predictive modeling, text and image mining ... video, audio, text and time series etc... Qualifications: * PhD degree in Computer Science ...

DevSelect

Principal Data Scientist

Redmond, WA

Understanding of machine learning techniques including predictive modeling, text and image mining ... video, audio, text and time series etc... Qualifications: * PhD degree in Computer Science ...

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All JobsAudio Machine Learning JobsAudio Machine Learning Jobs in Seattle, WA

Audio Machine Learning information

See Seattle, WA salary details

$33.6K

$96.1K

$195.2K

How much do audio machine learning jobs pay per year?

As of Jun 10, 2026, the average yearly pay for audio machine learning in Seattle, WA is $96,113.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,900.00 and $128,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Audio Machine Learning position, and why are they important?

To thrive in Audio Machine Learning, you need a strong background in machine learning, digital signal processing, and proficiency with programming languages such as Python or MATLAB, typically supported by a relevant degree in computer science, electrical engineering, or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with audio libraries (e.g., Librosa), and knowledge of cloud computing tools are highly valued, as are certifications in AI or data science. Strong problem-solving skills, creativity, and effective communication are essential soft skills for success in this field. These skills are crucial for developing innovative solutions, collaborating across multidisciplinary teams, and addressing complex audio data challenges in real-world projects.

What are the typical daily responsibilities of someone working in Audio Machine Learning?

Professionals in Audio Machine Learning typically spend their days designing, developing, and optimizing machine learning models tailored to audio data, such as speech or music recognition systems. You may also preprocess large datasets, extract and engineer relevant features, and collaborate closely with data scientists, audio engineers, and software developers to integrate your work into larger applications. Regular tasks often include running experiments, evaluating model performance, tuning hyperparameters, and keeping up with the latest advancements in the field. Team meetings, code reviews, and presenting findings to stakeholders are also common parts of the workweek.

What is an Audio Machine Learning job?

An Audio Machine Learning job involves developing algorithms and models that analyze, process, and generate audio data. Responsibilities typically include working with speech recognition, music analysis, sound classification, and audio enhancement. Professionals in this field use deep learning, signal processing, and neural networks to improve audio-based applications like voice assistants, noise reduction systems, and music recommendation engines. They often work with datasets of speech, music, or environmental sounds to build models that understand and manipulate audio signals effectively.

What are popular job titles related to Audio Machine Learning jobs in Seattle, WA? For Audio Machine Learning jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Audio Machine Learning jobs in Seattle, WA look for? The top searched job categories for Audio Machine Learning jobs in Seattle, WA are:
Audio Machine Learning jobs near you
Infographic showing various Audio Machine Learning job openings in Seattle, WA as of June 2026, with employment types broken down into 63% Full Time, 29% Part Time, 4% Temporary, and 4% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $96,113 per year, or $46.2 per hour.

Machine Learning Infra Engineer

Nuance Labs

Seattle, WA • On-site

Other

Posted 13 days ago

Job description

About the Role

Nuance Labs is building the next generation of emotionally expressive, real-time AI.

This is a critical role to build the infrastructure that powers our AI platform. You will own the systems that serve models at scale, orchestrate complex data workflows, and ensure our real-time video AI runs reliably with low latency for users worldwide.

 
Responsibilities

  • Own Inference Infrastructure: Build and maintain the serving stack for multimodal AI workloads. Optimize for latency, throughput, and cost using batching strategies, autoscaling, and intelligent resource allocation.

  • Real-Time Video Streaming: Architect systems to handle long-lived WebRTC connections with unpredictable client behavior, ensuring smooth video and audio delivery at scale.

  • Orchestrate Data Workflows: Build robust pipelines for offline processing, evaluation, and training using orchestration frameworks like Dagster or Ray. Manage petabyte-scale video storage and network requirements.

  • GPU Cluster Management: Configure and maintain GPU clusters using Kubernetes and Terraform. Implement monitoring, autoscaling based on custom metrics, and cost optimization strategies.

  • Developer Tooling: Build CI/CD, evaluation, and versioning systems that enable safe, zero-downtime model deployments and rapid iteration cycles.

Requirements

  • Infrastructure Expertise: Strong practical experience with Kubernetes, Terraform, and cloud platforms. You can design secure, scalable systems and debug complex distributed issues.

  • Systems Programming: Proficiency in Python and experience with systems languages (Rust or Go). Comfortable profiling workloads and resolving compute, memory, or network bottlenecks.

  • Orchestration & Pipelines: Experience managing large-scale offline workflows using tools like Dagster, Ray, Airflow, or similar frameworks.

  • Production Operations: Deep understanding of production reliability, monitoring, incident response, and capacity planning for high-traffic services.

Preferred Experience

  • Experience with WebRTC or real-time media pipelines in production

  • Experience running GPU-backed inference services at scale (vLLM, Triton Inference Server, TensorRT)

  • Knowledge of performance optimization and low-level systems debugging

  • Familiarity with video/audio processing and storage systems

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