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Remote Llm Engineer Intern Jobs in Washington (NOW HIRING)

Internship Program US (Remote)

Bethesda, MD · On-site +1

$18 - $23.25/hr

... intern to develop an understanding of how to create value through reducing operating costs in ... Unique skills in energy modeling, Excel, PowerPoint, data analytics, or engineering and design are ...

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Remote Llm Engineer Intern information

What is a remote LLM engineer intern?

A Remote LLM Engineer Intern is a student or early-career professional who works remotely to support the development and implementation of Large Language Models (LLMs), such as GPT or BERT. Their responsibilities often include data preprocessing, model training, fine-tuning, evaluation, and contributing to codebases for AI-driven applications. Interns collaborate with senior engineers and researchers, learning best practices in natural language processing and machine learning, while gaining hands-on experience with cutting-edge technologies. This role is typically offered by tech companies, research labs, or AI startups and allows interns to contribute to projects from anywhere with internet access.

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

To excel as a Remote LLM Engineer Intern, you need a solid background in computer science fundamentals, proficiency in Python, and experience with machine learning or natural language processing, often supported by relevant coursework or internships. Familiarity with deep learning frameworks such as PyTorch or TensorFlow, and version control systems like Git, is usually required. Strong problem-solving abilities, self-motivation, and the ability to communicate technical concepts remotely are essential soft skills. These competencies enable interns to contribute effectively to LLM projects, adapt to remote collaboration, and develop solutions in a rapidly evolving AI field.

What are the typical projects and responsibilities for a remote LLM engineer intern?

As a Remote LLM Engineer Intern, you can expect to work on tasks such as fine-tuning large language models, evaluating model outputs, and implementing improvements to existing NLP pipelines. Interns often collaborate with data scientists, machine learning engineers, and product teams to develop and test new features or workflows. A typical week may involve coding experiments, analyzing datasets, addressing model bias or performance issues, and participating in virtual team meetings. This role offers hands-on experience with state-of-the-art language models and exposure to the latest advancements in AI.

What is the difference between Remote Llm Engineer Intern vs Remote Machine Learning Engineer Intern?

AspectRemote Llm Engineer InternRemote Machine Learning Engineer Intern
Required CredentialsTypically pursuing or recent graduate in Computer Science, AI, or related fields; familiarity with NLP and LLMsSimilar educational background; focus on ML algorithms, data processing, and software engineering
Work EnvironmentRemote internship, often in tech companies developing NLP or AI productsRemote internship, in companies working on ML applications across industries
Employer & Industry UsageUsed in AI startups, research labs, and tech giants focusing on NLP and language modelsCommon in tech firms, research institutions, and companies deploying ML solutions

The main difference between a Remote Llm Engineer Intern and a Remote Machine Learning Engineer Intern lies in their focus areas. The Llm Intern specializes in language models and NLP-specific tasks, while the ML Intern has a broader scope in machine learning applications. Both roles require similar educational backgrounds and are typically remote internships in tech-driven environments.

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What job categories do people searching Remote Llm Engineer Intern jobs in Washington look for? The top searched job categories for Remote Llm Engineer Intern jobs in Washington are:
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Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Annapolis, MD • Remote

$121K - $159K/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.