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Temporary Large Language Model Llm Jobs (NOW HIRING)

Nvidia

OR

$98.40K - $125.40K/yr

We are now looking for a Senior Research Scientist passionate about Large Language Model (LLM) and Diffusion Language Model (DLM) post-training and system optimization. Are you excited to shape the ...

Pearson Education

Staff Software Engineer - AI

Hoboken, NJ · On-site

$150K - $180K/yr

Develop and integrate at least one Large Language Model (LLM) into production workflows. * Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering ...

Purple Drive Technologies

Gen AI architect

Mclean, VA · On-site

$63.75 - $84/hr

Lead the implementation of Large Language Model (LLM)-based systems and GenAI-powered applications. * Design and implement AI agents using frameworks such as LangChain. * Develop and integrate Model ...

Pearson Education

Staff Software Engineer - AI

Hoboken, NJ · On-site

$150K - $165K/yr

Develop and integrate at least one Large Language Model (LLM) into production workflows. * Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering ...

Virtusa Corporation

Python Data Engineer

Tampa, FL · On-site

$108.20K - $129.90K/yr

Evaluate, integrate, and work with Large Language Model (LLM) frameworks. * Collaborate with data scientists and business stakeholders to understand data requirements and translate them into ...

Pearson

Staff Software Engineer - AI

Hoboken, NJ · Hybrid

$150K - $165K/yr

Develop and integrate at least one Large Language Model (LLM) into production workflows. * Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering ...

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

As of May 29, 2026, the average hourly pay for temporary large language model llm in the United States is $24.34, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $29.09 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Large Language Model (LLM) Engineer, and why are they important?

To thrive as a Large Language Model (LLM) Engineer, you need a strong background in computer science, machine learning, and natural language processing, often supported by a relevant degree. Proficiency with tools like Python, TensorFlow or PyTorch, and experience with cloud platforms and version control systems is typically required. Strong problem-solving skills, attention to detail, and effective communication help engineers collaborate and innovate in complex projects. These skills are crucial for developing, fine-tuning, and deploying LLMs that deliver accurate and ethical AI solutions.

What are the typical challenges faced by professionals working in a Temporary Large Language Model (LLM) role, and how can they be addressed?

Professionals in temporary Large Language Model (LLM) roles often encounter challenges such as quickly adapting to new datasets, ensuring data privacy, and optimizing model performance within tight deadlines. Since these roles are project-based, there may be limited onboarding time, requiring a strong ability to learn and collaborate rapidly with cross-functional teams like data engineers and product managers. To succeed, it's helpful to be proactive in seeking clarification, documenting work thoroughly, and staying updated on the latest advancements in LLM technologies.

What are Temporary Large Language Model (LLM) roles?

Temporary Large Language Model (LLM) roles involve short-term positions where individuals work with or support the development, training, or deployment of large language models like GPT or similar AI technologies. These roles may include tasks such as data annotation, prompt engineering, model evaluation, or assisting in content moderation powered by LLMs. Temporary LLM roles are often project-based and can be found in tech companies, research labs, or organizations utilizing AI for various applications. They generally require familiarity with AI concepts, attention to detail, and sometimes programming skills.

What is the difference between Temporary Large Language Model Llm vs Data Scientist?

AspectTemporary Large Language Model LlmData Scientist
Required CredentialsTypically no formal degree, but expertise in AI/ML and programmingUsually requires a degree in Computer Science, Statistics, or related fields
Work EnvironmentAI development teams, research labs, tech companiesData analysis, modeling, and business insights in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, e-commerce, and more
Common Search & ComparisonFocuses on AI model deployment and developmentFocuses on data analysis and insights

The main difference is that a Temporary Large Language Model Llm is an AI system or model used for language processing, while a Data Scientist analyzes data to generate insights. The Llm is a tool or product, whereas the Data Scientist is a professional role that may utilize models like Llm in their work.

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Temporary Large Language Model Llm jobs near you
Infographic showing various Temporary Large Language Model Llm job openings in the United States as of May 2026, with employment types broken down into 3% As Needed, 24% Full Time, 68% Part Time, and 5% Contract. Highlights an 93% Physical, and 7% Remote job distribution, with an average salary of $50,625 per year, or $24.3 per hour.
Machine Learning Researcher, Foundation Models [SWE Org]

Machine Learning Researcher, Foundation Models [SWE Org]

Apple

Cupertino, CA

$147.40K - $272.10K/yr

Full-time

Medical, Dental, Retirement

Posted 14 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.
Description
We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning.
Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see your ideas improve the experience of billions of users.","responsibilities":"In this role, you will focus on pretraining, large language model (LLM) architecture, and scientific scaling of LLM. Experiences on full-stack LLM optimization such as mid-training, reinforcement learning, data research and kernel optimization (e.g. pallas and triton) will be a plus.
Preferred Qualifications
Code large language models.
Reinforcement learning, on-policy distillation.
Post-training, mid-training large language models.
LLM context lengthening.
Minimum Qualifications
Demonstrated expertise in deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP, ACL) or a track record in applying deep learning techniques to products
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
Ability to work in a collaborative environment.
PhD, or equivalent practical experience, in Computer Science, or related technical field.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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