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Remote Large Language Model Llm Jobs (NOW HIRING)

... remote within a mutually acceptable location. #LI-Hybrid Success Looks Like: * AI systems move ... Design and implement AI-powered applications including large language model (LLM) systems and ...

Hands-on experience with large language model (LLM) inference and/or model training using open ... Remote-friendly within the United States * Preference for candidates located near major East or ...

Develop and support Generative AI and Large Language Model (LLM) solutions, including model configuration, system prompts, classifiers, fine-tuning, content moderation, safety filters, and other ...

AI Engineer

OR · On-site +1

Develop and support Generative AI and Large Language Model (LLM) solutions, including model configuration, system prompts, classifiers, fine-tuning, content moderation, safety filters, and other ...

... large language model (LLM) development. You'll define what "good data" looks like for a range of ... This is a remote position (Washington candidates preferred). **We are not interested in working ...

... large language model (LLM) development. You'll define what "good data" looks like for a range of ... This is a remote position (Washington candidates preferred). What You'll Do * Conduct market and ...

San Francisco or Remote About The Role The NEAR AI team is building decentralized and confidential ... In this role, you will push the boundaries of how large language models are served. What You'll Be ...

San Francisco or Remote About The Role The NEAR AI team is building decentralized and confidential ... In this role, you will push the boundaries of how large language models are served. What You'll Be ...

... Large Language Model (LLM), or machine learning applications. * Strong programming experience in Python. * Experience developing APIs, backend services, and distributed systems. * Experience with ...

... Large Language Model (LLM), or machine learning applications. * Strong programming experience in Python. * Experience developing APIs, backend services, and distributed systems. * Experience with ...

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Remote Large Language Model Llm information

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

As of Sep 14, 2026, the average hourly pay for remote 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 is a remote large language model LLM?

A Remote Large Language Model (LLM) job involves working with advanced AI models, like GPT or similar, from a remote location. Professionals in these roles may develop, train, fine-tune, or implement large language models for various applications such as natural language processing, chatbots, or content generation. Remote LLM jobs can include positions like machine learning engineer, research scientist, or AI product manager. The work typically requires strong programming skills, experience with AI frameworks, and the ability to collaborate virtually with global teams.

What are the key skills and qualifications needed to thrive as a remote large language model LLM engineer?

To thrive as a Remote Large Language Model (LLM) Engineer, you need a strong background in computer science, machine learning, and natural language processing, typically supported by a relevant degree and experience with large-scale models. Proficiency with programming languages like Python, deep learning frameworks such as PyTorch or TensorFlow, and familiarity with cloud platforms and distributed systems are essential. Excellent problem-solving, communication, and collaboration skills are critical for remote teamwork and translating complex requirements into scalable solutions. These skills ensure the effective development, deployment, and maintenance of advanced language models in fast-evolving, distributed environments.

How does a remote large language model LLM engineer typically collaborate with cross-functional teams while working remotely?

Remote LLM Engineers often work closely with data scientists, product managers, and software engineers through virtual meetings, collaborative coding platforms, and shared documentation tools. Regular communication is key, with daily stand-ups or weekly syncs to align on project goals, update progress, and address challenges. They may also participate in code reviews, contribute to design discussions, and support model deployment efforts, all within a distributed team environment. This remote structure encourages self-motivation and proactive communication to ensure project success.

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

AspectRemote Large Language Model LlmData Scientist
Required CredentialsAdvanced degrees in AI, NLP, or related fields; experience with machine learning frameworksDegree in Data Science, Statistics, Computer Science, or related fields; strong analytical skills
Work EnvironmentPrimarily remote, focused on developing and fine-tuning language modelsRemote or on-site, analyzing data, building models, and generating insights
Employer & Industry UsageTech companies, AI research labs, startups working on NLP productsTech firms, finance, healthcare, marketing, and research organizations

While both roles involve data and machine learning, a Remote Large Language Model Llm specializes in developing and refining language models, whereas a Data Scientist focuses on analyzing data, building predictive models, and deriving insights across various domains.

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Infographic showing various Remote Large Language Model Llm job openings in the United States as of September 2026, with employment types broken down into 49% Full Time, 13% Part Time, and 38% Contract. Highlights an 100% Remote job distribution, with an average salary of $50,625 per year, or $24.3 per hour.

AI Technical Lead

Meridian, TX • On-site, Remote

Blue Cross of Idaho
Insurance Services • 501 - 1,000 employees

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 4 days ago


Key responsibilities

  • Design and deliver scalable AI systems, including data pipelines, model training workflows, and AI service layers.

  • Provide technical leadership, establish standards, and guide decision-making for AI system development.

  • Develop and deploy machine learning and generative AI solutions, ensuring performance, scalability, and operational efficiency.


Blue Cross of Idaho rating

6.0

Company rating: 6.0 out of 10

Based on 12 frontline employees who took The Breakroom Quiz


Job description

Our AI Technical Lead is responsible for designing and delivering scalable AI systems that enable intelligent applications across the organization. This role combines hands-on engineering, system architecture, and technical leadership to build production-grade machine learning and generative AI platforms. The Lead works closely with product, data engineering, and infrastructure teams to bring AI capabilities from experimentation into reliable production systems while supporting the organization's broader AI strategy and innovation initiatives.

Location: this position has preference to based in hybrid work location (onsite and WFH). There may be opportunity for fully remote within a mutually acceptable location. #LI-Hybrid

Success Looks Like:

  • AI systems move efficiently from experimentation and pilot phases into reliable production environments.

  • Engineering teams operate within clear architectural standards and scalable development practices.

  • AI capabilities deliver measurable business impact.

  • The organization is able to rapidly develop, test, and scale new AI-driven solutions

Key Responsibilities:

Technical Leadership

  • Provide technical leadership and mentorship to a team of AI engineers.

  • Establish engineering standards, coding practices, and architectural guidelines for AI system development.

  • Lead design reviews, guide technical decision making, and resolve complex engineering challenges.

  • Serve as a technical escalation point for AI system architecture and implementation

AI System Architecture

  • Architect end-to-end AI systems including data pipelines, model training workflows, AI service layers, and scalable AI application infrastructure.

  • Design and implement AI-powered applications including large language model (LLM) systems and retrieval-based knowledge applications.

  • Define architecture patterns that support experimentation, rapid prototyping, and production deployment of AI capabilities.

  • Develop service-based architectures that enable AI functionality to be integrated across enterprise applications.

AI Engineering & Development

  • Develop and deploy machine learning and generative AI solutions that support enterprise use cases.

  • Build reusable AI services and platform components that enable teams to rapidly develop and scale AI capabilities.

  • Implement evaluation, monitoring, and reliability systems to ensure consistent model performance.

  • Optimize AI pipelines for performance, scalability, and operational efficiency.

Cloud & MLOps

  • Design cloud-native infrastructure supporting AI and machine learning workloads.

  • Implement containerized AI services and automated deployment pipelines.

  • Support the development of scalable AI platforms that enable experimentation, model deployment, and operational monitoring.

  • Ensure AI systems follow best practices for reliability, observability, and cost management.

Collaboration & Delivery

  • Work closely with product managers, data engineers, and business stakeholders to identify and deliver high-value AI use cases.

  • Translate business requirements into scalable AI architecture and engineering solutions.

  • Partner with cross-functional teams to move AI solutions from pilots and experimentation into production environments.

  • Support initiatives that enable the organization to scale AI capabilities across multiple business domains.

Responsible AI & Governance

  • Promote responsible AI practices including transparency, fairness, and privacy considerations.

  • Implement safeguards and monitoring systems for AI applications operating in production.

  • Collaborate with security and compliance teams to ensure AI systems meet regulatory and organizational standards.

Required Education (must meet one of the following):

  • Bachelor or International Equivalency degree in Cybersecurity, Computer Science, Electrical Engineering, Information Systems, or closely related field of study; or equivalent work experience (Two years' relevant work experience is equivalent to one-year college)

  • Associate Degree in Computer Science, Electrical Engineering, Information Systems, or closely related field of study + 2 years additional experience

Required Experience: 6/+ years of experience in software engineering, machine learning engineering, and/or related AI/ML technical roles. Experience should include:

  • Experience designing and deploying machine learning or generative AI systems.

  • Strong programming experience in Python and modern backend technologies.

  • Experience building distributed systems or cloud-native architectures.

  • Experience implementing machine learning workflows or model deployment pipelines.

Preference for additional experience in:

  • Developing large language model (LLM) applications.

  • Experience with retrieval-based AI systems or knowledge-driven applications.

  • Working with cloud platforms and modern DevOps practices.

  • Mentoring engineers, leading technical initiatives, and/or serving as a technical lead

  • Working with large-scale data pipelines

As of the date of this posting, a good faith estimate of the current pay range is $118,506 - $177,758. The position is eligible for an annual incentive bonus (variable depending on company and employee performance). The pay range for this position takes into account a wide range of factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, relevant experience, skills, seniority, performance, travel requirements, internal equity, business or organizational needs, and alignment with market data. At Blue Cross of Idaho, it is not typical for an individual to be hired at or near the top range for the position. Compensation decisions are dependent on factors and circumstances at the time of offer.

We offer a robust package of benefits including paid time off, paid holidays, community service and self-care days, medical/dental/vision/pharmacy insurance, 401(k) matching and non-contributory plan, life insurance, short and long term disability, education reimbursement, employee assistance plan (EAP), adoption assistance program and paid family leave program.

We will adhere to all relevant state and local laws concerning employee leave benefits, in line with our plans and policies.

Reasonable accommodations

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.


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