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Ai Voice Trainer information

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How much do ai voice trainer jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for ai voice trainer in the United States is $24.74, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $26.44 per hour, depending on experience, location, and employer.

What is the difference between Ai Voice Trainer vs Speech Language Pathologist?

AspectAi Voice TrainerSpeech Language Pathologist
CredentialsTypically requires training in AI, machine learning, and voice technology; certifications varyRequires a master's degree in speech-language pathology and state licensure
Work EnvironmentTech companies, AI development labs, remote or office settingsHospitals, clinics, schools, private practices
Industry UsagePrimarily in AI and tech industries focusing on voice recognition and synthesisHealthcare and rehabilitation sectors focusing on speech and language disorders

While both roles involve working with voice, an Ai Voice Trainer focuses on developing and refining voice AI systems using technology and machine learning. In contrast, a Speech Language Pathologist works directly with individuals to diagnose and treat speech and language disorders. The roles differ in credentials, work environment, and industry focus, but both aim to improve voice communication.

What are some common challenges faced by AI voice trainers when improving speech recognition accuracy?

AI Voice Trainers often encounter challenges such as handling diverse accents, dialects, and background noises that can affect speech recognition models. Ensuring that training data is inclusive and representative of real-world usage is crucial, as is working closely with linguists and engineers to fine-tune models. Additionally, maintaining user privacy and addressing potential biases in voice datasets are ongoing priorities, requiring collaborative problem-solving and continuous learning.

What are the key skills and qualifications needed to thrive as an AI voice trainer?

To thrive as an AI Voice Trainer, you need a strong background in linguistics, phonetics, or computational linguistics, often supported by a relevant degree or experience in speech technology. Familiarity with annotation tools, speech recognition systems, and machine learning platforms is typically required. Excellent attention to detail, cross-cultural communication, and analytical thinking are crucial soft skills for refining voice data and training conversational AI. These skills ensure accurate, inclusive, and natural-sounding AI voice models, directly impacting product usability and user satisfaction.

What is an AI voice trainer?

An AI Voice Trainer is a professional who helps develop, refine, and improve the performance of voice recognition and generation systems powered by artificial intelligence. They work by collecting, annotating, and analyzing voice data, as well as training AI models to better understand human speech, accents, and emotions. Their role is crucial in ensuring that voice assistants, speech-to-text applications, and other AI-powered voice technologies are accurate, inclusive, and user-friendly.
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Cities with the most Ai Voice Trainer job openings:

What states have the most Ai Voice Trainer jobs?

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What job categories do people searching Ai Voice Trainer jobs look for?

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Ai Voice Trainer jobs near you
Infographic showing various Ai Voice Trainer job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, 13% Part Time, 7% Temporary, and 13% Contract. Highlights an 46% In-person, 7% Hybrid, and 47% Remote job distribution, with an average salary of $51,453 per year, or $24.7 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Annapolis, MD • On-site

$140 - $180/hr

Other

Re-posted 2 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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