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Remote Llm Trainer Jobs in Indiana (NOW HIRING)

Candidates who are not within commuting distance of a Crowe office may be considered for a remote ... and training; licensure and certifications; and other business and organizational needs. The ...

Candidates who are not within commuting distance of a Crowe office may be considered for a remote ... and training; licensure and certifications; and other business and organizational needs. The ...

Candidates who are not within commuting distance of a Crowe office may be considered for a remote ... and training; licensure and certifications; and other business and organizational needs. The ...

Candidates who are not within commuting distance of a Crowe office may be considered for a remote ... and training; licensure and certifications; and other business and organizational needs. The ...

Candidates who are not within commuting distance of a Crowe office may be considered for a remote ... and training; licensure and certifications; and other business and organizational needs. The ...

Master's in Taxation or LLM strongly preferred * Active CPA license required * Minimum of 8 years ... Candidates who are not within commuting distance of a Crowe office may be considered for a remote ...

Master's in Taxation or LLM strongly preferred * Active CPA license required * Minimum of 8 years ... Candidates who are not within commuting distance of a Crowe office may be considered for a remote ...

Candidates who are not within commuting distance of a Crowe office may be considered for a remote ... and training; licensure and certifications; and other business and organizational needs. The ...

Remote Llm Trainer information

What is a remote LLM trainer?

Remote LLM Trainers are professionals who work from any location to help train large language models (LLMs) by providing high-quality data, evaluating model outputs, and refining model behavior. They may annotate data, review AI-generated content, or design prompts and tasks to improve the model's performance. These roles are crucial in ensuring that LLMs become more accurate, safe, and useful across various applications. Remote LLM Trainers often have backgrounds in language, linguistics, data science, or related fields and rely on digital tools to collaborate with AI development teams.

What does a remote LLM trainer do?

As a Remote LLM Trainer, your workday often involves creating, curating, and reviewing datasets, developing prompts, and evaluating large language model outputs for quality and safety. Much of your collaboration happens asynchronously through digital channels—such as project management tools, messaging platforms, and regular video meetings—with researchers, data scientists, and fellow trainers. You may also participate in feedback sessions to discuss model behavior and share insights on improving training methodologies. Adapting to rapidly evolving project requirements and maintaining clear communication are key to success in this distributed, fast-paced environment.

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

AspectRemote Llm TrainerData Scientist
Required CredentialsBackground in AI, NLP, or machine learning; often a degree in computer science or related fieldDegree in computer science, statistics, or related fields; often certifications in data analysis or machine learning
Work EnvironmentRemote, collaborative teams developing and fine-tuning language modelsRemote or on-site, analyzing data, building models, and deriving insights
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting, and research organizations

While both roles involve working with data and machine learning, a Remote Llm Trainer specializes in training and refining language models, whereas a Data Scientist focuses on analyzing data, building predictive models, and deriving insights across various industries.

What are the key skills and qualifications needed to thrive as a remote LLM trainer?

To thrive as a Remote LLM Trainer, you need a deep understanding of machine learning, natural language processing, and large language models, typically supported by a degree in computer science or related fields. Experience with Python, deep learning frameworks like TensorFlow or PyTorch, and familiarity with annotation tools or data labeling platforms is essential. Strong communication, attention to detail, and the ability to work independently are standout soft skills in this role. These skills and qualities ensure accurate model training, effective collaboration with distributed teams, and the delivery of high-quality AI solutions.

What are the most commonly searched types of Llm Trainer jobs in Indiana?

The most popular types of Llm Trainer jobs in Indiana are:

What are popular job titles related to Remote Llm Trainer jobs in Indiana?

For Remote Llm Trainer jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Remote Llm Trainer jobs in Indiana look for?

The top searched job categories for Remote Llm Trainer jobs in Indiana are:

What cities in Indiana are hiring for Remote Llm Trainer jobs?

Cities in Indiana with the most Remote Llm Trainer job openings:

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Indianapolis, IN • Remote

$117K - $154K/yr

Full-time

Re-posted 21 hours 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.