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Audio Machine Learning Jobs in North Carolina (NOW HIRING)

... machine learning modeling, etc.) to provide actionable insights that improve business outcomes and ... Visual / Audio / Speaking Able to access and interpret client information received from the ...

... machine learning modeling, etc.) to provide actionable insights that improve business outcomes and ... Visual / Audio / Speaking Able to access and interpret client information received from the ...

DSP Engineer

Raleigh, NC

$125K - $180K/yr

Experience with machine learning/artificial intelligence a plus. * Familiarity with software ... audio to RF. * Ability to perform bench-level bring-up and troubleshooting of data acquisition ...

DSP Engineer with Security Clearance

Raleigh, NC ยท On-site

$127K - $149K/yr

Experience with machine learning/artificial intelligence a plus. โ€ข Familiarity with software ... from audio to RF. โ€ข Ability to perform bench-level bring-up and troubleshooting of data ...

DSP Engineer

Raleigh, NC ยท On-site

$125K - $180K/yr

Experience with machine learning/artificial intelligence a plus. * Familiarity with software ... audio to RF. * Ability to perform bench-level bring-up and troubleshooting of data acquisition ...

DSP Engineer

Raleigh, NC ยท On-site

$125K - $180K/yr

Experience with machine learning/artificial intelligence a plus. * Familiarity with software ... audio to RF. * Ability to perform bench-level bring-up and troubleshooting of data acquisition ...

Senior Google CX Engineer (GECX)

Durham, NC ยท On-site +1

$116K - $146K/yr

... CCaaS audio ingestion methods such as SIPREC and gRPC, as well as mobile applications and web ... Machine Learning Engineer, or Professional Data Engineer. * Practical expertise with Google ...

The Wolak Learning Commons is open Monday - Thursday 8am to 6pm and Friday 8am to 430pm. Essential ... Maintain Penmen Print machines around campus * Document issues to be reported in our ticketing ...

New

Senior Production Technician

Durham, NC

$16.75 - $20.75/hr

You'll join a collaborative environment where professionalism, learning, and respect are core to ... Understanding of digital audio systems , networking-based audio transport, and digital audio ...

Senior Production Technician

Durham, NC ยท On-site

$16.75 - $20.75/hr

You'll join a collaborative environment where professionalism, learning, and respect are core to ... Understanding of digital audio systems , networking-based audio transport, and digital audio ...

... learning opportunities and development for our teams GENERAL PURPOSE: To assess equipment/machinery ... Frequently reaching, bending, stooping, talking, hearing (audio equipment), handling objects with ...

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Audio Machine Learning information

See North Carolina salary details

$26.8K

$76.8K

$155.9K

How much do audio machine learning jobs pay per year?

As of Jul 30, 2026, the average yearly pay for audio machine learning in North Carolina is $76,753.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,400.00 and $102,700.00 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.

Will MLE be replaced by AI?

In the context of an Audio Machine Learning (ML) role, AI tools and automation are increasingly used to assist with tasks like data processing and model deployment. However, MLE professionals are essential for designing, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Human expertise remains critical for interpreting results and ensuring system performance.

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.

Which 5 jobs will survive AI?

Audio Machine Learning specialists are likely to continue in demand as AI advances because their expertise in developing and refining audio recognition systems requires specialized skills that are difficult to automate fully. Roles involving creative audio design, audio engineering, and human oversight of AI systems are also expected to persist. These jobs often require a combination of technical knowledge, domain expertise, and critical thinking that AI cannot easily replace.

What engineer makes $500,000 a year?

Senior audio machine learning engineers with extensive experience, advanced skills in deep learning and signal processing, and often working at large tech companies or specialized research labs can earn salaries approaching or exceeding $500,000 annually. Compensation typically includes base salary, bonuses, and stock options, especially in high-demand industries like AI and audio processing.

Do audio engineers get paid well?

Audio engineers typically earn competitive salaries that vary based on experience, location, and industry sector. Entry-level positions may start lower, but experienced professionals working in recording studios, broadcasting, or live sound often have higher earnings, especially with specialized skills and certifications. Overall, the profession offers the potential for good compensation, particularly for those with technical expertise and a strong portfolio.
What are the most commonly searched types of Audio Machine Learning jobs in North Carolina? The most popular types of Audio Machine Learning jobs in North Carolina are:
What are popular job titles related to Audio Machine Learning jobs in North Carolina? For Audio Machine Learning jobs in North Carolina, the most frequently searched job titles are:
What job categories do people searching Audio Machine Learning jobs in North Carolina look for? The top searched job categories for Audio Machine Learning jobs in North Carolina are:
Infographic showing various Audio Machine Learning job openings in North Carolina as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $76,753 per year, or $36.9 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Raleigh, NC โ€ข Remote

$119K - $157K/yr

Full-time

Posted 15 days ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health ยท Remote (US) ยท Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.