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Llm Engineer Remote Jobs (NOW HIRING)

Mutual of Omaha

AI Engineer - Remote

Omaha, NE · Remote

Nebraska, Remote Categories: Information Technology Mutual Of Omaha is seeking an experienced ... Guide teams in adopting and building LLM-powered applications, including context engineering ...

NAVA Software Solutions

AI Engineer -Remote

Houston, TX · On-site +1

AI Engineer Location: 100% Remote Duration: 6+ month contract-to-hire Requirement: * Implemented ... LLM optimization & improved response handling. Responsibilities: * Design, develop, and deploy ...

OmegaHires

AI / Machine Learning Engineer

$80 - $100/hr

AI / ML Engineer - Remote (US) Location: Remote - United States Employment type: Contract (6-12 ... remote US team. Responsibilities * Build and improve ML models and LLM-powered features (RAG ...

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How much do llm engineer remote jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for llm engineer remote in the United States is $53.63, according to ZipRecruiter salary data. Most workers in this role earn between $43.27 and $62.26 per hour, depending on experience, location, and employer.

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

AspectLlm Engineer RemoteData Scientist Remote
Required CredentialsAdvanced degree in CS, ML, or related field; experience with NLP and deep learningDegree in CS, Statistics, or related; experience with data analysis and machine learning
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesData analysis, model development, reporting; across various industries
Employer & Industry UsageTech companies, AI startups, research institutionsFinance, healthcare, tech, e-commerce, and more

While both roles involve machine learning, Llm Engineers focus on developing large language models and NLP applications, often requiring deep expertise in AI research. Data Scientists analyze data to inform business decisions, with broader industry applications. The roles share some credentials but differ in focus and daily tasks.

What are some typical challenges faced by remote LLM Engineers when collaborating with cross-functional teams?

Remote LLM Engineers often work closely with data scientists, product managers, and software engineers to develop and deploy large language models. One common challenge is ensuring clear and consistent communication across different time zones and technical backgrounds, which can sometimes lead to misaligned project goals or delays. To overcome this, many teams rely on detailed documentation, regular virtual meetings, and collaborative project management tools. Building strong relationships remotely and proactively sharing updates can make collaboration smoother and more productive.

What are the key skills and qualifications needed to thrive as an LLM Engineer in a remote role, and why are they important?

To thrive as an LLM Engineer remotely, you need strong expertise in machine learning, natural language processing, and proficiency with programming languages such as Python, often supported by a degree in computer science or related fields. Familiarity with frameworks like PyTorch or TensorFlow, experience with cloud platforms (AWS, GCP), and knowledge of large language model architectures are commonly required. Excellent problem-solving skills, self-motivation, and effective remote communication make candidates stand out. These capabilities are crucial for developing, deploying, and maintaining advanced language models while collaborating efficiently with distributed teams.

What is an LLM Engineer (Remote)?

An LLM Engineer, or Large Language Model Engineer, is a professional who designs, develops, and optimizes applications using advanced AI language models such as GPT-4 or similar technologies. Working remotely, they are responsible for integrating these models into products, fine-tuning them for specific tasks, and ensuring their performance and reliability. LLM Engineers often collaborate with data scientists, software developers, and product managers to create solutions in areas like chatbots, content generation, and natural language processing. Their work requires a strong background in machine learning, programming, and cloud computing.
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Llm Engineer Remote jobs near you
Infographic showing various Llm Engineer Remote job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, 1% Part Time, and 4% Contract. Highlights an 76% Physical, 5% Hybrid, and 19% Remote job distribution, with an average salary of $111,552 per year, or $53.6 per hour.

GenAI / LLM Engineer - Remote (should be able to work on PST time zones)

Rootshell Enterprise Technologies, Inc.

Remote

Other

Posted 17 days ago

Job description

GenAI/LLM Engineer
Remote (should be able to work on PST time zones)
Prefers local to bay area.
Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs-particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.
  • Enables domain-specific fine-tuning of models to Client unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:
  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to Client domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:
  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content

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