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Remote Llm Training Jobs in Raleigh, NC (NOW HIRING)

Bandwidth

Director, Strategic AI Partnerships

Raleigh, NC · Remote

  • Medical

  • Dental

  • Vision

  • PTO

Design and execute the framework for licensing Bandwidth's unique voice data assets for AI training ... Open to remote candidates, however relatively frequent travel to Bandwidth HQ in Raleigh will be ...

CIMA

Psychometrician

Durham, NC · Remote

$80K - $95K/yr

  • Medical

  • Retirement

  • PTO

... LLM implementations in test development and psychometrics * 0 - 1 years of experience in ... training. Learn more about The Association on LinkedIn and our Career Site. #LI-Remote ...

Redhat

Senior AI FinOps Data Analyst

Raleigh, NC · On-site +1

$88K - $141K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... g., Cursor, LLM APIs) * Familiarity with enterprise data platforms (Snowflake, Dataverse, or ... For positions with Remote-US locations, the actual salary range for the position may differ based ...

Redhat

Senior Software Engineer

Raleigh, NC · On-site +1

$159K - $195K/yr

Must have one (1) year of experience with Agentic Frameworks and LLM. #LI-DNI The salary range for ... For positions with Remote-US locations, the actual salary range for the position may differ based ...

All JobsLlm Training JobsRemote Llm Training JobsRemote Llm Training Jobs in Raleigh, NC

Remote Llm Training information

See Raleigh, NC salary details

$14

$41

$75

How much do remote llm training jobs pay per hour?

As of Aug 15, 2026, the average hourly pay for remote llm training in Raleigh, NC is $41.03, according to ZipRecruiter salary data. Most workers in this role earn between $27.12 and $52.36 per hour, depending on experience, location, and employer.

What are common challenges in remote LLM training, and how can they be addressed?

Professionals in remote LLM (Large Language Model) training roles often face challenges such as managing distributed team communication, ensuring data privacy, and handling large-scale computational resources. Staying organized with asynchronous collaboration tools and maintaining clear documentation can help streamline teamwork. Additionally, understanding cloud-based infrastructure and adhering to strict data security protocols are essential for handling sensitive datasets. Regular check-ins and knowledge-sharing sessions also foster a supportive and productive remote work environment.

What is the difference between Remote Llm Training vs Data Scientist?

AspectRemote Llm TrainingData Scientist
Required CredentialsKnowledge of NLP, machine learning, programming skillsStatistics, programming, domain expertise
Work EnvironmentRemote, collaborative teams, AI/ML companiesRemote or on-site, diverse industries
Industry UsageAI development, NLP projectsData analysis, predictive modeling

Remote Llm Training focuses on developing and fine-tuning large language models, requiring expertise in NLP and machine learning. Data Scientists analyze data to extract insights and build models across various industries. While both roles involve programming and data skills, Remote Llm Training is specialized in AI model development, whereas Data Scientists work on broader data analysis tasks.

What is remote LLM training?

Remote LLM training refers to the process of training large language models (LLMs), such as GPT or similar AI models, on distributed computing resources that are accessed remotely. This allows data scientists and AI engineers to leverage powerful hardware, like GPUs or TPUs, which may not be available locally. Remote LLM training is commonly used to handle the massive computational requirements of modern AI models and enables collaboration among teams in different locations. It also provides scalability, flexibility, and cost-effectiveness for organizations working on advanced AI projects.

What skills and qualifications are needed for remote LLM training?

To excel in Remote LLM Training, you need a strong background in machine learning, natural language processing, and computer science, often demonstrated by a relevant degree or industry experience. Familiarity with frameworks like PyTorch or TensorFlow, experience with large-scale data management, and knowledge of distributed computing systems are typically required. Strong problem-solving skills, effective communication, and the ability to work independently are vital soft skills in this remote, collaborative environment. These competencies ensure efficient model training, high-quality output, and seamless teamwork across distributed teams.

What are popular job titles related to Remote Llm Training jobs in Raleigh, NC?

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What job categories do people searching Remote Llm Training jobs in Raleigh, NC look for?

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What cities near Raleigh, NC are hiring for Remote Llm Training jobs?

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Remote Llm Training jobs near you
Infographic showing various Remote Llm Training job openings in Raleigh, NC as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 19% Part Time, and 3% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $85,348 per year, or $41 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Raleigh, NC • Remote

$119K - $157K/yr

Full-time

Re-posted yesterday

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.

Apply