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Remote Ai Voice Jobs (NOW HIRING)

About the role We are looking for a Lead Engineer, AI Agent Voice Experience to help build the next ... Remote work setup budget or work-from-home stipend * Monthly wellness and communication allowance

... or remote About OhMD OhMD is an AI patient communication platform used by thousands of healthcare organizations. Our product suite includes two-way patient texting, an AI voice and text assistant ...

Responsibilities Remote Contact Center Leadership * Lead, scale, and continuously optimize a remote ... Own the performance of AI-powered support functions including AI voice (conversational IVR ...

Responsibilities Remote Contact Center Leadership * Lead, scale, and continuously optimize a remote ... Own the performance of AI-powered support functions including AI voice (conversational IVR ...

Responsibilities Remote Contact Center Leadership * Lead, scale, and continuously optimize a remote ... Own the performance of AI-powered support functions including AI voice (conversational IVR ...

$120K - $140K/yr

... AI and driven to deliver seamless, intuitive customer experiences. This is a remote opportunity to ... Connect voice bots to internal APIs, CRMs, and knowledge bases to power real-time interactions.

Voice Bot Engineer

$120K - $140K/yr

... AI and driven to deliver seamless, intuitive customer experiences. This is a remote opportunity to ... Connect voice bots to internal APIs, CRMs, and knowledge bases to power real-time interactions.

Responsibilities Remote Contact Center Leadership * Lead, scale, and continuously optimize a remote ... Own the performance of AI-powered support functions including AI voice (conversational IVR ...

Showing results 41-60

Remote Ai Voice information

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$5

$48

$76

How much do remote ai voice jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for remote ai voice in the United States is $48.17, according to ZipRecruiter salary data. Most workers in this role earn between $39.18 and $60.10 per hour, depending on experience, location, and employer.

What is the difference between Remote Ai Voice vs Remote Speech Recognition Specialist?

AspectRemote Ai VoiceRemote Speech Recognition Specialist
Required CredentialsBackground in AI, machine learning, or linguistics; often a degree in related fieldsExpertise in speech technology, linguistics, or audio processing; certifications in speech systems are common
Work EnvironmentCollaborative remote teams developing AI voice models and datasetsRemote roles focused on optimizing and troubleshooting speech recognition systems
Industry UsageUsed in AI development, virtual assistants, and voice-enabled applicationsApplied in improving speech recognition accuracy for various platforms

Remote Ai Voice roles focus on developing and training AI voice models, while Remote Speech Recognition Specialists optimize and troubleshoot speech systems. Both roles require expertise in linguistics and AI but differ in their primary focus within the voice technology industry.

What is a remote AI voice?

A Remote AI Voice job typically involves creating, training, or managing artificial intelligence systems that process or generate human-like speech. Professionals in this role may work on voice assistants, automated customer service, transcription, or voice synthesis projects, all from a remote location. Common responsibilities include developing speech recognition models, improving natural language understanding, and testing voice-based applications. This role often requires skills in machine learning, linguistics, and software development, along with experience in AI voice technologies.

What are the key skills and qualifications needed to thrive as a remote AI voice specialist, and why are they important?

To thrive as a Remote AI Voice Specialist, you need expertise in speech recognition technologies, natural language processing, and a background in computer science or linguistics. Familiarity with tools such as Python, TensorFlow, and major cloud AI platforms, as well as certifications in AI or machine learning, is typically required. Strong problem-solving, communication, and collaboration skills help you effectively interpret user needs and work across distributed teams. These abilities are crucial to developing accurate, user-friendly AI voice solutions that enhance user experiences in remote and digital environments.

How does a remote AI voice professional typically collaborate with developers and product teams?

As a Remote AI Voice professional, you’ll regularly work alongside developers, product managers, and UX designers to integrate voice technologies into applications and products. Collaboration often involves participating in sprint meetings, providing feedback on voice model performance, and troubleshooting issues related to speech recognition or synthesis. Clear communication and documentation are key, as you'll likely work across time zones and use tools like Slack, Jira, or Zoom to coordinate efforts. This cross-functional teamwork ensures that the voice AI features align with user needs and product goals.
More about Remote Ai Voice jobs
What cities are hiring for Remote Ai Voice jobs? Cities with the most Remote Ai Voice job openings:
What are the most commonly searched types of Ai Voice jobs? The most popular types of Ai Voice jobs are:
What states have the most Remote Ai Voice jobs? States with the most job openings for Remote Ai Voice jobs include:
Infographic showing various Remote Ai Voice job openings in the United States as of August 2026, with employment types broken down into 6% Internship, 55% Full Time, 28% Part Time, and 11% Contract. Highlights an 100% Remote job distribution, with an average salary of $100,198 per year, or $48.2 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Hartford, CT • Remote

$123K - $162K/yr

Full-time

Posted 28 days ago


Job description

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

AMC Health · Remote (US) · 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.