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Sciforium

LLM Training Engineer

San Francisco, CA · On-site

$155K - $220K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

About the Role As an LLM Training Engineer , you'll work across the full foundation-model stack: pretraining and scaling , post-training and Reinforcement Learning , sandbox environments for ...

Spellbrush

AI Anime Researcher - LLM

San Francisco, CA · On-site

Required : • You have published or have industry experience with LLM post-training • You have a deep understanding of LLM training and evaluation with attention to detail. • You have worked on ...

Mercor

Project Manager - LLM Specialist

New York, NY · Remote

$50 - $60/hr

Apply an analytical mindset to improve LLM training projects . * Own unfamiliar problems from start to finish. Qualifications Must-Have * Strong organizational skills. * Analytical mindset. * High ...

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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.
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Llm Training jobs near you
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.

LLM Training Engineer

Sciforium

San Francisco, CA • On-site

$155K - $220K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 14 days ago

Job description

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
About the Role
As an LLM Training Engineer, you'll work across the full foundation-model stack: pretraining and scaling, post-training and Reinforcement Learning, sandbox environments for evaluation and agentic learning, and deployment + inference optimization. You'll build and iterate quickly on research ideas, contribute production-grade infrastructure, and help deliver models that can serve real-world use cases at scale.
What you'll work on
This role spans multiple tracks - candidates may focus on one or contribute across several. Examples include:
Pretraining & Scaling
  • Train large byte-native foundation models across massive, heterogeneous corpora
  • Design stable training recipes and scaling laws for novel architectures
  • Improve throughput, memory efficiency, and utilization on large GPU clusters
  • Build and maintain distributed training infrastructure and fault-tolerant pipelines

Post-training & RL
  • Develop post-training pipelines (SFT, preference optimization, RLHF/RLAIF, RL)
  • Curate and generate targeted datasets to improve specific model capabilities
  • Build reward models and evaluation frameworks to drive iterative improvement
  • Explore inference-time learning and compute techniques to enhance performance

Sandbox Environments & Evaluation
  • Build scalable sandbox environments for agent evaluation and learning
  • Create realistic, high-signal automated evals for reasoning, tool use, and safety
  • Design offline + online environments that support RL-style training at scale
  • Instrument environments for observability, reproducibility, and iteration speed

Deployment & Inference Optimization
  • Optimize inference throughput/latency for byte-native architectures
  • Build high-performance serving pipelines (KV caching, batching, quantization, etc.)
  • Improve end-to-end model efficiency, cost, and reliability in production
  • Profile and optimize GPU kernels, runtime bottlenecks, and memory behavior

Ideal candidate credentials
Technical strength
  • Strong general software engineering skills (writing robust, performant systems)
  • Experience with training or serving large neural networks (LLMs or similar)
  • Solid grasp of deep learning fundamentals and modern literature
  • Comfort working in high-performance environments (GPU, distributed systems, etc.)

Relevant experience (one or more)
  • Pretraining / large-scale distributed training (FSDP/ZeRO/Megatron-style systems)
  • Post-training pipelines (SFT, RLHF/RLAIF, preference optimization, eval loops)
  • Building RL environments, simulators, or agent frameworks
  • Inference optimization, model compression, quantization, kernel-level profiling
  • Building large ETL pipelines for internet-scale data ingestion and cleaning
  • Owning end-to-end production ML systems with monitoring and reliability

Research orientation
  • Ability to propose and evaluate research ideas quickly
  • Strong experimental hygiene: ablations, metrics, reproducibility, analysis
  • Bias toward building - you can turn ideas into working code and results

Education
  • MS or PhD in Computer Science, Machine Learning, AI, Mathematics, or related field

Benefits include
  • Medical, dental, and vision insurance
  • 401k plan
  • Daily lunch, snacks, and beverages
  • Flexible time off
  • Competitive salary and equity

Equal opportunity
Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

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