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Ai Audio Jobs in Raleigh, NC (NOW HIRING)

Varsity Tutors

Digital Media Tutor

Raleigh, NC · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... graphic design basics, audio production, web content, animation fundamentals, and digital ...

Varsity Tutors

Digital Media Tutor

Chapel Hill, NC · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... graphic design basics, audio production, web content, animation fundamentals, and digital ...

Varsity Tutors

Digital Media Tutor

Durham, NC · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... graphic design basics, audio production, web content, animation fundamentals, and digital ...

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Showing results 1-20

All JobsAi Audio JobsAi Audio Jobs in Raleigh, NC

Ai Audio information

See Raleigh, NC salary details

$28.7K

$82.1K

$166.7K

How much do ai audio jobs pay per year?

As of Aug 23, 2026, the average yearly pay for ai audio in Raleigh, NC is $82,098.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,600.00 and $109,800.00 per year, depending on experience, location, and employer.

What is an AI audio engineer?

An AI Audio Engineer is a professional who uses artificial intelligence and machine learning technologies to develop, enhance, or analyze audio content. Their work may include creating AI-driven tools for music production, speech recognition, audio restoration, or sound synthesis. AI Audio Engineers often collaborate with software developers, musicians, and sound designers to integrate intelligent audio solutions into various applications. This field requires knowledge of audio engineering, signal processing, and programming skills.

What are the key skills and qualifications needed to thrive as an AI audio engineer?

To thrive as an AI Audio Engineer, you need strong foundations in audio signal processing, machine learning, and computer programming, often supported by a degree in computer science, electrical engineering, or a related field. Familiarity with tools such as Python, TensorFlow or PyTorch, digital audio workstations (DAWs), and version control systems is typically required. Attention to detail, creativity, and effective collaboration are standout soft skills for this role. These skills and qualities are essential for developing innovative audio solutions and ensuring seamless integration of AI technologies in audio applications.

How does an AI audio specialist typically collaborate with other teams during a project?

AI Audio specialists often work closely with product managers, software engineers, and UX designers to integrate audio solutions into products or platforms. Collaboration usually involves joint meetings to define project requirements, frequent communication to troubleshoot integration issues, and sharing feedback to refine audio models. A successful AI Audio professional is proactive in bridging technical and creative perspectives, ensuring that audio features meet user needs and technical standards. This team-oriented environment fosters learning and can open pathways to roles in project leadership or advanced technical development.

What is the difference between Ai Audio vs Voiceover Artist?

AspectAi AudioVoiceover Artist
Required CredentialsTechnical skills, AI and audio editing knowledgeVoice training, acting skills, demo reels
Work EnvironmentDigital, remote, tech-focusedRecording studios, remote, live performances
Industry UsageMedia production, AI development, tech companiesAdvertising, entertainment, media
Search & Comparison IntentTechnical, AI-driven audio solutionsCreative voice work, acting

Ai Audio involves creating audio content using artificial intelligence technology, focusing on automation and digital tools. Voiceover Artists provide human voice recordings for various media, emphasizing performance and acting skills. While Ai Audio is tech-based and automated, Voiceover Artists rely on vocal talent and creativity. Both roles are essential in media production but serve different purposes and skill sets.

What are popular job titles related to Ai Audio jobs in Raleigh, NC?

For Ai Audio jobs in Raleigh, NC, the most frequently searched job titles are:

What job categories do people searching Ai Audio jobs in Raleigh, NC look for?

The top searched job categories for Ai Audio jobs in Raleigh, NC are:

What cities near Raleigh, NC are hiring for Ai Audio jobs?

Cities near Raleigh, NC with the most Ai Audio job openings:

Ai Audio jobs near you
Infographic showing various Ai Audio job openings in Raleigh, NC as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 69% Physical, 4% Hybrid, and 27% Remote job distribution, with an average salary of $82,098 per year, or $39.5 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Raleigh, NC • On-site

$150 - $210/hr

Other

Re-posted 9 days ago

Job description

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

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

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

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