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Remote Audio Signal Processing Machine Learning Jobs

$121K - $231K/yr

For additional information on remote work at Penn State, seeNotice to Out of State Applicants. POSITION SPECIFICS We are searching for a motivated and talented Signal Processing Research and ...

Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar ... remote servers and services, virtual computers and clusters. * Proficiency in signal processing ...

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar ... remote servers and services, virtual computers and clusters. * Proficiency in signal processing ...

Work with signal processing data and time-series analysis * Improve local development and CI/CD for ... Onsite / Remote / Flexible work arrangements or hybrid options (position dependent) * Relocation ...

They are seeking a skilled Machine Learning Engineer to build and deploy production ML systems for ... signal processing data and time-series analysis • Improve local development and CI/CD for ML ...

Remote We are seeking an Applied Machine Learning Engineer with a strong focus on practical ... audio analysis, melody generation, and process automation. • Uphold ethical AI practices ...

Audio Engineer

$120K - $160K/yr

Own the end-to-end audio signal chain and post-processing pipeline for all collection programs ... Specify and validate recording setups for vendors and remote contributors (signal-chain testing in ...

Senior ML/Research Scientist

Mountain View, CA · On-site +1

$116K - $148K/yr

Apply signal processing, machine learning, and statistical analysis to decode biosignals. * Develop ... Mountain View, CA (not a remote position) Employment Eligibility: At this time NextSense is only ...

Audio Visual Engineer III

Rockville, MD · On-site +1

$95K - $110K/yr

Digital Signal Processors (DSPs) * Professional audio systems * Professional cameras system * Control systems * Program and troubleshoot Crestron, Extron, Q-SYS, Evertz, Analog Way, Biamp, Ross ...

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

Design, train, evaluate, and deploy machine learning models across text, image, audio, and ... Natural Language Processing: LLMs, text classification, information extraction, retrieval systems ...

Showing results 21-40

Remote Audio Signal Processing Machine Learning information

See salary details

$29.5K

$84.5K

$171.5K

How much do remote audio signal processing machine learning jobs pay per year?

As of Jul 28, 2026, the average yearly pay for remote audio signal processing machine learning in the United States is $84,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $113,000.00 per year, depending on experience, location, and employer.

What is the difference between Remote Audio Signal Processing Machine Learning vs Remote Audio Engineering?

AspectRemote Audio Signal Processing Machine LearningRemote Audio Engineering
Required CredentialsKnowledge of machine learning, signal processing, programming (Python, MATLAB)Audio engineering certifications, audio production experience
Work EnvironmentResearch labs, tech companies, remote collaborationRecording studios, broadcast companies, remote or onsite
Industry UsageDeveloping algorithms for audio enhancement, noise reduction, speech recognitionMixing, mastering, live sound, audio content creation

Remote Audio Signal Processing Machine Learning focuses on developing algorithms using machine learning techniques to improve audio quality and analysis. In contrast, Remote Audio Engineering involves practical audio production, mixing, and recording tasks. Both roles require audio knowledge, but the former emphasizes programming and data science, while the latter centers on sound quality and production skills.

More about Remote Audio Signal Processing Machine Learning jobs
What cities are hiring for Remote Audio Signal Processing Machine Learning jobs? Cities with the most Remote Audio Signal Processing Machine Learning job openings:
What are the most commonly searched types of Audio Signal Processing Machine Learning jobs? The most popular types of Audio Signal Processing Machine Learning jobs are:
What states have the most Remote Audio Signal Processing Machine Learning jobs? States with the most job openings for Remote Audio Signal Processing Machine Learning jobs include:
Infographic showing various Remote Audio Signal Processing Machine Learning job openings in the United States as of July 2026, with employment types broken down into 76% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $84,456 per year, or $40.6 per hour.
Data Scientist - Signal Processing Engineer (Acoustics)

Data Scientist - Signal Processing Engineer (Acoustics)

Cutsforth, LLC

Ferndale, WA • Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago


Job description

Role Information:
  • Job Title: Data Scientist - Signal Processing Engineer- (Acoustics)
  • Work Location: Fully remote position, home office
  • Employment Type: Full-time
  • Employment Status: Exempt, salaried
  • Visa sponsorship is not available for this position.
  • Must reside in the United States.
  • We are not accepting applicants for remote workers in California, Illinois, and New York at this time.
Compensation:
  • $98,837 - $175,000, depending on years of experience
Role Overview:Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment. Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications. Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
Key Responsibilities:
  • Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
  • Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
  • Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
  • Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
  • Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
  • Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
  • Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
  • Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
  • Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
  • Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.
Required Qualifications:
  • Bachelor’s degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Acoustical Engineering, Mechanical Engineering, Aerospace Engineering, or a closely related engineering discipline required.
  • 5+ years of professional experience in data science, machine learning, or applied signal processing, with demonstrated work on electrical, current/voltage, or industrial sensor signal data.
  • Direct industry experience in one or more of: Industrial/Rotating Equipment, Power Systems, Electrical Machine Diagnostics, or Condition Monitoring.
  • Hands-on experience with time-series and signal processing techniques, including spectral analysis, filtering, and feature extraction from raw sensor data.
  • Proficiency in Python, including scientific computing libraries (NumPy, SciPy, pandas) and ML frameworks (scikit-learn, PyTorch, or TensorFlow).
  • Familiarity with electrical measurement and analysis workflows (e.g., current/voltage waveform capture, power quality analyzers, or equivalent instrumentation).
  • Strong analytical and problem-solving skills with the capacity to work through ambiguous or data-sparse problem spaces.
  • Excellent written and verbal communication skills; ability to present technical findings to non-technical audiences.
Preferred Qualifications:
  • Master’s degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Data Science, or a related field.
  • Experience with Electrical Signature Analysis (ESA), Motor Current Signature Analysis (MCSA), or similar electrical machine diagnostic techniques.
  • Familiarity with rotating machinery fault physics (bearing fault frequencies, eccentricity, winding faults, broken rotor bars).
  • Demonstrated ability to own an ML model from prototype through production, including monitoring and retraining.
  • Familiarity with array/multi-sensor signal fusion across electrical and vibration domains.
  • Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tooling (MLflow, Docker, Airflow, CI/CD pipelines).
  • Experience with physics-informed modeling approaches.
  • Active participation in the broader signal processing or data science community through publications, open-source projects, or conference presentations.
Other Qualifications:
  • Successfully pass background check for cybersecurity site access.
  • Strong foundation in signal processing theory and application, including experience with electrical, acoustic, or time-series data in a professional setting.
  • Proficiency in Python for data manipulation, signal processing, and model development (NumPy, SciPy, pandas, scikit-learn, PyTorch or TensorFlow).
  • Ability to work with uncertainty and incomplete information; comfortable forming and testing hypotheses when ground truth is limited.
  • Clear communicator capable of translating technical signal processing and ML findings to non-specialist audiences.
  • Self-directed and effective working remotely across cross-functional teams.
  • Must reside in the United States; not accepting applicants in California, Illinois, or New York.
Cybersecurity Role Expectations:
  • Candidate will be responsible for reviewing policies and procedures related to cybersecurity and those relevant to the functions of their role.
  • Candidate is expected to maintain a cybersecure work environment.
Benefits:
  • Paid Time Off
  • Medical, Vision, Dental Insurance
  • Health Savings Account with Employer contributions
  • 401(k) with Employer match
  • Short-term & Long-term Disability Coverage
  • Accidental Death & Dismemberment Coverage
  • Life Insurance Coverage
  • Eight paid holidays per year
  • All other benefits required by applicable law

Alignment with Corporate Values

All Cutsforth employees are expected to perform their work in a manner that exhibits understanding and adherence to the Company Mission and Core Attributes of Cutsforth Employees. Employees in management roles must exhibit continual improvement along Cutsforth’s Leadership Traits. Further, each employee must read and adhere to corporate policies and safety protocols.

  • Learn more about Cutsforth here, including our Mission & Values: Cutsforth.com/About

Equal Employment Opportunity Statement:

Cutsforth will not discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, or national origin. Cutsforth will take affirmative action to ensure that applicants are employed, and that employees are treated during employment, without regard to their race, color, religion, sex, sexual orientation, gender identity, or national origin. Such action shall include, but not be limited to the following: Employment, upgrading, demotion, or transfer, recruitment or recruitment advertising; layoff or termination; rates of pay or other forms of compensation; and selection for training, including apprenticeship. Cutsforth agrees to post in conspicuous places, available to employees and applicants for employment, notices to be provided by the provisions of this nondiscrimination clause.

For Cutsforth's full Equal Employment Opportunity Policy, click here: EEO Notice to Employees & Applicants

California Privacy Notice:
If you are a California resident, please review our California Job Applicant Privacy Policy for details regarding the personal information we collect during the hiring process, how we use it, and your rights under the CCPA. By submitting your application, you acknowledge that you have read and understand our privacy practices.
For Cutsforth's full CCPA Privacy Policy, click here CCPA: California Privacy Notice to Applicants

Washington State Fair Chance Act:

Cutsforth considers all qualified applicants, including those with criminal histories, in accordance with the Washington State Fair Chance Act. We do not automatically exclude applicants because of a criminal record. Any criminal background check occurs only after a conditional offer of employment, and any resulting decision is based on an individualized assessment of the record's relationship to the specific job.

Learn more about your rights and our process here: Fair Chance Act

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