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Remote Llm Researcher Jobs in Indiana (NOW HIRING)

Join a National Top Workplace Named a Top Workplace in the USA and Top Remote Workplace, Kobie is ... This is not a research role. You'll prototype, ship, monitor, and iterate on features used by real ...

State and Local Tax Manager

Indianapolis, IN · On-site +1

$102K - $209K/yr

Responsibilities include researching complex multi-state tax issues, identifying planning ... Master's in Taxation or JD/LLM a plus * CPA or JD required * 5+ years of prior public accounting ...

State and Local Tax Manager

South Bend, IN · On-site +1

$102K - $209K/yr

Responsibilities include researching complex multi-state tax issues, identifying planning ... Master's in Taxation or JD/LLM a plus * CPA or JD required * 5+ years of prior public accounting ...

Master's in Taxation or LLM strongly preferred * Active CPA license required * Minimum of 8 years ... Excellent writing, communication, and tax research skills * Demonstrated leadership experience ...

Master's in Taxation or LLM strongly preferred * Active CPA license required * Minimum of 8 years ... Excellent writing, communication, and tax research skills * Demonstrated leadership experience ...

This position can be remote based in the United States. The company is headquartered and based in ... Lead engagements around federal and state income tax, pass-through entities, R&D credits, and ...

Remote Llm Researcher information

What are the key skills and qualifications needed to thrive as a remote LLM researcher, and why are they important?

To thrive as a Remote LLM Researcher, you need a strong background in machine learning, natural language processing, and deep learning, typically supported by an advanced degree in computer science or a related field. Familiarity with frameworks like PyTorch or TensorFlow, experience working with large language models (LLMs), and knowledge of distributed computing tools are commonly required. Outstanding problem-solving abilities, communication skills, and the ability to work independently are essential soft skills for remote collaboration and research innovation. These skills enable effective development, evaluation, and deployment of advanced language models in a distributed team environment.

How do remote LLM researchers typically collaborate with cross-functional teams given their distributed work environment?

Remote LLM Researchers often work closely with data scientists, machine learning engineers, and product managers via virtual collaboration tools such as Slack, Zoom, and GitHub. Regular meetings, shared documentation, and project management platforms help maintain clear communication and alignment on research goals. Being proactive in sharing updates, seeking feedback, and participating in code reviews is essential for seamless teamwork. This collaborative approach ensures research findings can be effectively integrated into products and services, despite the physical distance.

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

AspectRemote Llm ResearcherRemote Data Scientist
CredentialsAdvanced degrees in AI, NLP, or related fields; research experienceDegree in Data Science, Statistics, or Computer Science; often includes certifications
Work EnvironmentResearch-focused, often in AI labs or tech companies, remote options availableData analysis, modeling, and visualization tasks, remote or on-site
Industry UsageAI research, NLP development, machine learning innovationBusiness analytics, predictive modeling, data-driven decision making

Remote Llm Researchers focus on developing and improving large language models through research and experimentation, often in AI labs. Remote Data Scientists analyze data to generate insights and build predictive models. While both roles require strong technical skills, Remote Llm Researchers are more research-oriented, whereas Remote Data Scientists focus on applying data techniques to solve business problems.

What is a remote LLM researcher?

A Remote LLM Researcher is a professional who studies and develops large language models (LLMs), such as GPT or BERT, while working from a location outside of a traditional office setting. Their work typically involves conducting experiments, analyzing data, improving model architectures, and publishing findings in the field of natural language processing (NLP). Remote LLM Researchers often collaborate with colleagues online, use cloud-based computing resources, and contribute to advancements in AI language technologies. This role requires strong programming skills, a background in machine learning, and the ability to work independently in a distributed team environment.
What are the most commonly searched types of Llm Researcher jobs in Indiana? The most popular types of Llm Researcher jobs in Indiana are:
What are popular job titles related to Remote Llm Researcher jobs in Indiana? For Remote Llm Researcher jobs in Indiana, the most frequently searched job titles are:
What cities in Indiana are hiring for Remote Llm Researcher jobs? Cities in Indiana with the most Remote Llm Researcher job openings:
Infographic showing various Remote Llm Researcher job openings in Indiana as of August 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 76% Physical, 5% Hybrid, and 19% Remote job distribution.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Indianapolis, IN • Remote

$117K - $154K/yr

Full-time

Posted 22 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.