1

Llm Training Jobs (NOW HIRING)

Own the end-to-end creation of pre-training datasets for LLMs. This includes defining the mix of ... Experience building massive LLM training sets from scratch, including raw web crawls (e.g., Common ...

Showing results 21-40

Llm Training information

See salary details

$32K

$68.7K

$112K

How much do llm training jobs pay per year?

As of Aug 20, 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.

What cities are hiring for Llm Training jobs?

Cities with the most Llm Training job openings:

What are the most commonly searched types of Llm Training jobs?

The most popular types of Llm Training jobs are:

What states have the most Llm Training jobs?

States with the most job openings for Llm Training jobs include:

Infographic showing various Llm Training job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 16% Part Time, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $68,682 per year, or $33 per hour.

Intermediate/ Senior Software Engineer - Cortex LLM Training Platform

Snowflake Inc.

Bellevue, WA • On-site

$138K - $182K/yr

Other

Posted 20 days ago


Job description

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don't just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset - who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
Senior Software Engineer - Cortex Training
The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput.
The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it.
YOU WILL:
  • Design and build across the full stack - from the public training APIs and SDK through the control plane to the GPU data plane.
  • Scale the distributed systems that make GPU compute serverless - multi-tenant scheduling, placement, and capacity-aware routing across regional GPU pools, with fault tolerance built in.
  • Drive end-to-end performance at scale - keep the training, inference, and RL loops fast and the data plane responsive under heavy concurrent load, with GPUs kept saturated.
  • Productionize research building blocks - partner with Snowflake Research to turn state-of-the-art training and inference techniques into reliable, composable components customers can run at enterprise scale.
QUALIFICATIONS:
  • 3 + years (Intermediate) | 6+ years (Senior) building and shipping production ML systems
  • Strong distributed systems and infrastructure foundation - designing scalable, fault-tolerant services and operating them on Kubernetes in production.
  • Familiarity with GPU and LLM infrastructure - e.g., PyTorch, DeepSpeed/FSDP, Ray, CUDA/NCCL, vLLM; able to debug across the data, infrastructure, and GPU layers.
  • Demonstrated ability to harden complex systems for reliability, throughput, and cost efficiency.
  • BS in Computer Science or a related field (MS/PhD a plus).
  • (Bonus) Hands-on LLM post-training / modeling experience - the strongest candidates pair deep infra skills with real post-training intuition.

Snowflake is growing fast, and we're scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com