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Llm Training Jobs (NOW HIRING)

The engineer works with senior engineers and researchers on AI training and inference systems, focusing on LLM execution engines, data and KV‑cache management, and multi‑tier memory hierarchies ...

Experience with LLM training, fine-tuning, and inference optimization. * Proficiency in Python, cloud AI services, and distributed training. * Familiarity with retrieval-augmented generation (RAG ...

Manage distributed infrastructure for multi-GPU LLM training * Profiling and optimizing training and inference code * Deploy efficient inference pipelines for serving LLMs at scale Requirements: * B.

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$32K

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How much do llm training jobs pay per year?

As of Sep 14, 2026, the average yearly pay for llm training in the United States is $68,682.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $84,500.00 per year, depending on experience, location, and employer.

What is an LLM Training?

An LLM Training job involves developing, fine-tuning, and optimizing large language models (LLMs) to improve their performance and accuracy. This role typically includes data collection, preprocessing, model training, evaluation, and troubleshooting issues related to bias, efficiency, and scalability. Professionals in this field work with machine learning frameworks, large datasets, and computational resources to enhance AI capabilities. They may also collaborate with researchers, engineers, and product teams to deploy models for real-world applications.

What are the key skills and qualifications needed to thrive in the LLM Training position?

To excel in LLM Training, you need a strong background in machine learning, natural language processing (NLP), and computer science, often backed by an advanced degree in a related field. Experience with programming languages such as Python, frameworks like PyTorch or TensorFlow, and familiarity with data annotation tools are essential, along with knowledge of cloud platforms and distributed computing. Strong analytical thinking, effective communication, and the ability to collaborate across interdisciplinary teams set top candidates apart. These skills ensure high-quality model development, efficient project execution, and the ability to adapt to evolving AI technologies.

What types of teams or professionals does an LLM Training specialist typically collaborate with?

Professionals specializing in LLM Training often work closely with data engineers, software developers, domain experts, product managers, and quality assurance analysts. Collaboration is essential for collecting and preprocessing training data, integrating models into products, and ensuring outputs meet business and user requirements. These roles frequently participate in agile project workflows, contribute to cross-functional team meetings, and collaborate on continuous model improvements. Engaging with diverse teams expands your understanding of product goals and helps you deliver robust and reliable language models that align with organizational objectives.

Is it possible to train Llm Training?

Training large language models (LLMs) is possible but requires significant computational resources, expertise in machine learning, and access to large datasets. It typically involves using specialized hardware like GPUs or TPUs and knowledge of frameworks such as TensorFlow or PyTorch. Many organizations opt to fine-tune pre-trained models rather than train from scratch due to the high costs and complexity involved.

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Infographic showing various Llm Training job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 79% Full Time, 19% Part Time, and 1% Contract. Highlights an 84% Physical, 1% Hybrid, and 15% Remote job distribution, with an average salary of $68,682 per year, or $33 per hour.

Staff Machine Learning Research Engineer, Agent Post-training - Enterprise GenAI

Seattle, WA • On-site

Scale AI
Software Development • 201 - 500 employees

Full-time

Re-posted 19 days ago


Scale AI rating

8.5

Company rating: 8.5 out of 10

Based on 9 frontline employees who took The Breakroom Quiz


Job description

Job Summary:
Scale AI is a leading AI data foundry focused on accelerating the development of AI applications. The Staff Machine Learning Research Engineer will build out next-gen Agent RL training platforms and integrate cutting-edge research into the training stack to serve enterprise clients.
Responsibilities:
• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers.
• Research cutting edge algorithms to integrate directly into our training stack.
• Design solutions that enable complex multi-agent systems to directly learn from both process + outcome based rewards.
Preferred:
• 5+ years of LLM training in a production environment
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field
Company:
Scale’s mission is to develop reliable AI systems for the world’s most important decisions. Founded in 2016, the company is headquartered in San Francisco, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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