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Audio Machine Learning Jobs in Washington (NOW HIRING)

NPR

Lead Data Scientist, AI Labs

Washington, DC · On-site

Design and architect automated machine learning pipelines to transform decades of unstructured audio, transcripts, and text to support automated semantic metadata generation. * Collaborate with the ...

Socket.dev

Lead Data Scientist, AI Labs

Washington, DC · On-site

$164 - $201/hr

Design and architect automated machine learning pipelines to transform decades of unstructured audio, transcripts, and text to support automated semantic metadata generation. * Collaborate with the ...

GDIT

Data Scientist - TS/SCI w/Poly

Mclean, VA · On-site

$212K - $287K/yr

Create and maintain custody of production machine learning models across a variety of tasks, including but not limited to audio extraction, object recognition, Natural Language Processing (NLP), and ...

Cmu

AI Security Software Engineer

Arlington, VA · On-site +1

Familiarity with applied machine learning domains (e.g., natural language processing, computer vision, autonomy, audio analysis) * Experience and knowledge in cybersecurity best practices

10a Labs

Engineering Fellowship

Washington, DC · On-site

$125/hr

... Machine Learning Engineering: * * Computer vision skills (OCR, image classification, deep fake detection) * Familiarity with multimodal learning (text-image or text-audio) or cross-domain model ...

10a Labs

Engineering Fellowship

Washington, DC · On-site

... Machine Learning Engineering: * * Computer vision skills (OCR, image classification, deep fake detection) * Familiarity with multimodal learning (text-image or text-audio) or cross-domain model ...

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Showing results 21-40

All JobsAudio Machine Learning JobsAudio Machine Learning Jobs in Washington

Audio Machine Learning information

See Washington salary details

$33.4K

$95.7K

$194.2K

How much do audio machine learning jobs pay per year?

As of Aug 20, 2026, the average yearly pay for audio machine learning in Washington is $95,654.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,600.00 and $128,000.00 per year, depending on experience, location, and employer.

What is an audio machine learning?

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 does an audio machine learning do?

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 are the key skills and qualifications needed to thrive in audio machine learning?

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 most commonly searched types of Audio Machine Learning jobs in Washington?

The most popular types of Audio Machine Learning jobs in Washington are:

What are popular job titles related to Audio Machine Learning jobs in Washington?

For Audio Machine Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Audio Machine Learning jobs in Washington look for?

The top searched job categories for Audio Machine Learning jobs in Washington are:

What cities in Washington are hiring for Audio Machine Learning jobs?

Cities in Washington with the most Audio Machine Learning job openings:

Audio Machine Learning jobs near you
Infographic showing various Audio Machine Learning job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 18% Part Time, 2% Temporary, and 5% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $95,654 per year, or $46 per hour.

AI Security Software Engineer

Carnegie Mellon University

Arlington, VA • On-site

Full-time

Re-posted 3 days ago

Job description

Job Summary:
Carnegie Mellon University is seeking an AI Security Software Engineer within the CERT Division of the Software Engineering Institute. The role involves developing machine learning-based prototypes and tools for AI security applications, collaborating with researchers to design experimental solutions, and applying software engineering best practices to build scalable systems.
Responsibilities:
• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team
Qualifications:
Required:
• BS in computer science, machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years of experience; OR PhD in the same fields with two (2) years of experience
• Understanding of software engineering principles and system design
• Experience with containerization and microservices architectures
• Travel to various locations to support the SEI’s overall mission. This includes within the SEI and CMU community, sponsor sites, conferences, and offsite meetings on occasion (5%)
• You will be subject to a background check and will need to obtain and maintain a Department of War (DoW) security clearance
• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team
Preferred:
• Experience applying statistical modeling and advanced data analytics techniques
• Background in developing AI/ML solutions in real-world settings
• Familiarity with applied machine learning domains (e.g., natural language processing, computer vision, autonomy, audio analysis)
• Experience and knowledge in cybersecurity best practices
• Demonstrated ability to quickly learn and adapt to new technologies and domains
Company:
Carnegie Mellon University is a research university offering programs and research across engineering, science, arts, and business. Founded in 1900, the company is headquartered in Pittsburgh, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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