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Entry Level Large Language Model Llm Jobs (NOW HIRING)

JPMorgan Chase & Co.

AI/ML Software Engineer II

Manhattan, NY · On-site

$107K - $147K/yr

Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. * Experience of all LLM models and their capabilities * Experience with ...

TiTANS Consulting

WI · On-site

Ontwikkelen van oplossingen op basis van Large Language Models (LLM's) en Vision Language Models (VLM's). * Fine-tunen van AI-modellen en optimaliseren van prompts voor specifieke use cases.

CIM Group, LP

Data Science Intern

Los Angeles, CA · On-site

Prototype GenAI and large language model (LLM) applications-such as retrieval-augmented and agentic workflows-that make firm data easier to explore, analyze, and act on. * Partner with our MLOps ...

CIM Group, LP

Data Science Intern

Los Angeles, CA · On-site

Prototype GenAI and large language model (LLM) applications-such as retrieval-augmented and agentic workflows-that make firm data easier to explore, analyze, and act on. * Partner with our MLOps ...

TransCore

Sr Software Development Engineer

Round Rock, TX · On-site

$114K - $150K/yr

Apply AI-assisted development tools and large language model (LLM) capabilities to accelerate delivery, and contribute to the design, integration, and evaluation of AI/LLM-enabled product features.

TransCore

Sr Software Development Engineer

Round Rock, TX · On-site

$114K - $150K/yr

Apply AI-assisted development tools and large language model (LLM) capabilities to accelerate delivery, and contribute to the design, integration, and evaluation of AI/LLM-enabled product features.

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How much do entry level large language model llm jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for entry level large language model llm in the United States is $22.48, according to ZipRecruiter salary data. Most workers in this role earn between $19.47 and $24.76 per hour, depending on experience, location, and employer.

What is an entry level large language model LLM?

An Entry Level Large Language Model (LLM) role typically refers to positions where individuals work with advanced AI systems, like ChatGPT or similar models, to support tasks such as data annotation, model evaluation, prompt engineering, or customer support. Entry-level LLM professionals might help train models, test outputs for accuracy, or assist with basic research. These roles usually require strong analytical skills, attention to detail, and some familiarity with AI concepts, but do not always require advanced programming experience. They offer a great starting point for those interested in the field of artificial intelligence and natural language processing.

What types of projects do entry level large language model LLM engineers typically contribute to?

Entry-level professionals in LLM roles often support data preparation, model fine-tuning, and evaluation tasks under the guidance of more experienced engineers or data scientists. They may annotate data, help run experiments, monitor model outputs for quality, and assist in deploying models for internal testing or limited production use. Collaboration with cross-functional teams—including machine learning engineers, product managers, and research scientists—is common, offering valuable exposure to various stages of the LLM development lifecycle. This hands-on experience helps build foundational skills and prepares individuals for more advanced responsibilities in the field.

What are the key skills and qualifications needed to thrive as an entry level large language model LLM engineer?

To thrive as an Entry Level Large Language Model (LLM) Engineer, you need a solid background in computer science, machine learning fundamentals, and proficiency in programming languages like Python, typically supported by a relevant degree. Familiarity with machine learning frameworks (such as PyTorch or TensorFlow), version control systems, and cloud computing platforms is often required. Strong analytical thinking, problem-solving skills, and effective communication set candidates apart in this role. These competencies are crucial for developing, fine-tuning, and deploying LLMs to ensure innovative and reliable AI solutions.

What is the difference between Entry Level Large Language Model Llm vs Data Analyst?

AspectEntry Level Large Language Model LlmData Analyst
Required CredentialsBasic understanding of NLP, programming skills (Python), coursework or certifications in AI/MLBachelor's degree in Data Science, Statistics, or related field; often certifications in data analysis tools
Work EnvironmentResearch labs, AI companies, tech startups; focus on model development and trainingBusiness environments, consulting firms, finance, healthcare; focus on data interpretation and reporting
Industry UsageAI development, NLP applications, machine learning researchBusiness intelligence, market analysis, operational insights

Entry Level Large Language Model Llm roles focus on developing and training NLP models, requiring programming and AI knowledge. Data Analysts interpret data to inform business decisions, often using statistical tools. While both roles involve working with data, Llm positions are more technical and research-oriented, whereas Data Analysts focus on data interpretation and reporting.

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Entry Level Large Language Model Llm jobs near you
Infographic showing various Entry Level Large Language Model Llm job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $46,753 per year, or $22.5 per hour.

Research Scientist

Sunnyvale, CA • On-site

MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)
See more jobs at MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)external

Full-time

Re-posted 22 days ago

Job description

Job Summary:
MBZUAI is a dedicated research lab focused on advancing AI through foundational models. As a Research Scientist, you will work on data-centric large language model (LLM) development, leading research and implementation efforts to enhance reasoning capabilities and integrate cutting-edge research with robust engineering.
Responsibilities:
• Lead research and implementation of reasoning-enhanced LLM capabilities through novel data collection, architecture design, and system integration.
• Design and implement pipelines to collect, curate, and structure open-source and web-scale data relevant to reasoning tasks, ensuring scalability and reproducibility.
• Build robust software to support fine-tuning, evaluation, and deployment of LLMs that interact with structured and unstructured knowledge bases.
• Collaborate with ML researchers to create, test, and evaluate new approaches in information retrieval, agentic search, and RAG (retrieval-augmented generation) pipelines.
• Rapidly prototype tools, APIs, and infrastructure for enabling LLMs to reason over external information, and build datasets for identifying and analyzing LLM failure modes.
• Communicate research findings in internal documents and external publications (e.g., top-tier conferences like ACL, ICLR, NeurIPS).
• Contribute to design/code reviews and foster engineering best practices in a high-performance research environment.
• Represent MBZUAI at conferences and forums, promoting institutional leadership in safe, efficient, and high-impact AI systems.
• Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Qualifications:
Required:
• Master’s in Computer Science, Data Science, or a related technical field, or equivalent practical experience required.
• Experience working with large language models, including fine-tuning, prompt engineering, and multi-modal interaction.
• Strong Python development skills with a focus on research-grade code and scalable data pipelines.
• Familiarity with collecting and processing large-scale datasets from open-source and web resources.
• Demonstrated ability to work with ML infrastructure (e.g., model evaluation, optimization, debugging).
• Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.
• Effective communication and collaboration skills for working in cross-functional teams.
Preferred:
• PhD or equivalent research experience in Machine Learning, NLP, or Data Science with a focus on reasoning and LLMs preferred.
• Experience designing and deploying agentic LLM systems, reasoning benchmarks, or RAG pipelines.
• Background in building complex knowledge retrieval systems (e.g., knowledge graphs, semantic search, indexing).
• Strong publication record in leading AI conferences (e.g., ICLR, ACL, NeurIPS, EMNLP).
• Familiarity with performance constraints in production environments and the trade-offs in model and data design.
• Prior contributions to open-source ML research or data tools.
Company:
Official account of Mohamed bin Zayed University of Artificial Intelligence. Dedicated to research, innovation, and empowering brilliant minds in AI. Founded in 2019, the company is headquartered in Abu Dhabi, ARE, with a team of 51-200 employees. The company is currently Growth Stage.

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