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Audio Machine Learning Jobs in Washington (NOW HIRING)

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Audio Machine Learning information

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$33.4K

$95.7K

$194.2K

How much do audio machine learning jobs pay per year?

As of Sep 10, 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, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $95,654 per year, or $46 per hour.

Data Scientist - Tech - Top Secret required to apply - DC area

Reston, VA • On-site

Bow-Wave-LLC
See more jobs at Bow-Wave-LLCexternal

Other

Re-posted 8 days ago

Job description

Conducts data analytics, data engineering, data mining, exploratory analysis, predictive analysis, and statistical analysis, and uses scientific techniques to correlate data into graphical, written, visual and verbal narrative products, enabling more informed analytic decisions.

Proactively retrieves information from various sources, analyzes it for better understanding about the data set, and builds AI tools that automate certain processes. Duties typically include:

Responsibilities
  • Create data packages in the form of databases, reports, and visualizations.
  • Communicate ongoing data science activities, technical findings, and data products for both technical and non‑technical customers.
  • Extract relevant features from large data stores containing open source, PIA, and CAI data that may have bad records, partial records, errors, or other forms of noise.
  • Extract features from open source information stored in a wide range of possible formats, including JSON, XML, raw text logs, industry‑specific encodings, and graph link data.
  • Apply natural language processing, computer vision, signal processing, and speaker and speech recognition algorithms to identify objects in text, image, video, and audio files.
  • Apply descriptive and inferential statistics to describe data and make predictions about the data, including statistical tests to determine confidence for a hypothesis, common summary statistics (e.g., mean, variance, and counts), fit distributions to datasets, and use those distributions to predict event likelihoods.
  • Execute data science methods using parallel computing frameworks such as deeplearning4j, Torch, TensorFlow, Caffe, Neon, NVIOFFICE CUDA Deep Neural Network library (cuDNN), and OpenCV, as well as distributed data processing frameworks (e.g., Hadoop, including HDFS, HBase, Hive, Impala, Giraph, Sqoop, Spark, including MLlib, GraphX, SQL, and DataFrames).
  • Execute data science methods using common programming and scripting languages: Python, Java, Scala, and R (statistics).
Qualifications
  • Skilled in data visualization and use of graphical applications such as Microsoft Power BI and Tableau.
  • Proficient in major data science languages such as R and Python, and in managing and merging disparate data sources, preferably through R, Python, or SQL.
  • Experience with large data multi‑INT analytics, machine learning, and automated predictive analytics.
  • Ability to build AI tools and automated processes such as recommendation engines or automated lead‑scoring systems.
  • Strong background in statistical analysis and data mining algorithms.
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