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Flexible Llm Training Jobs (NOW HIRING)

Sciforium

LLM Training Engineer

San Francisco, CA · On-site

$155K - $220K/yr

About the Role As an LLM Training Engineer , you'll work across the full foundation-model stack ... Flexible time off * Competitive salary and equity Equal opportunity Sciforium is an equal ...

Sciforium

LLM Dataset Engineer

San Francisco, CA · On-site

$155K - $210K/yr

Post-Training & Alignment Data: Lead the development of high-quality post-training datasets ... Flexible time off * Competitive salary and equity Equal opportunity Sciforium is an equal ...

Alinia AI

Applied AI Research Intern

... LLM post-training. * Experience with explainable AI (XAI). Why Join Alinia? * Cutting-edge tech: Work on one of the most important challenges in AI--alignment, safety, and trust * Flexible work:

C the Signs

AI Data Engineer

Boston, MA · Remote

$117K - $140K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

Sciforium

GPU Kernel Engineer

San Francisco, CA · On-site

$190K - $250K/yr

Experience working with large-scale LLM workloads (training or inference). Nice-to-Haves ... Flexible time off * Competitive salary and equity Equal opportunity Sciforium is an equal ...

Samaya AI

Applied Scientist (ML)

Mountain View, CA · Hybrid

$190K - $275K/yr

LLM post-training and reinforcement learning * AI agents for knowledge workflows * ML benchmarks ... Access comprehensive health insurance, including medical, dental, vision, flexible spending account ...

C the Signs

AI Data Engineer

New York, NY · Remote

$117K - $140K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

AI Data Engineer

Boston, MA · On-site +1

$124K - $149K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

AI Data Engineer

Boston, MA · On-site +1

$124K - $149K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

AI Data Engineer

New York, NY · On-site +1

$125K - $150K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

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How much do flexible llm training jobs pay per year?

As of Aug 14, 2026, the average yearly pay for flexible llm training in the United States is $77,988.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,000.00 and $89,500.00 per year, depending on experience, location, and employer.

What are common challenges in flexible LLM training, and how can they be managed?

Professionals in flexible LLM training roles often encounter challenges such as managing diverse data sources, adapting to rapidly evolving model architectures, and ensuring efficient use of computational resources. Balancing experimentation with reproducibility and collaborating across interdisciplinary teams can also be demanding. To address these challenges, it's important to implement robust workflow automation, stay updated on new research, and foster open communication with data engineers and machine learning specialists. Leveraging cloud-based tools and adhering to best practices in documentation can further streamline the training process.

What is flexible LLM training?

Flexible LLM (Large Language Model) Training refers to customizable and adaptable methods for training AI models, particularly large language models, to suit specific needs or constraints. This approach allows users to adjust training parameters, data sources, and model architectures, making it easier to optimize models for different tasks, hardware, or data availability. Flexible LLM training is useful for organizations wanting to fine-tune models for specialized domains or improve efficiency in resource-limited environments.

What is the difference between Flexible Llm Training vs Data Scientist?

AspectFlexible Llm TrainingData Scientist
Required CredentialsTypically requires knowledge of machine learning, NLP, and programming skillsRequires degrees in statistics, computer science, or related fields, often with programming skills
Work EnvironmentOften involves training models in cloud or research labs, collaborative teamsData analysis, modeling, and visualization in corporate or research settings
Industry UsageUsed in AI development, NLP projects, and machine learning researchApplied across industries for data analysis, predictive modeling, and decision-making

Flexible Llm Training focuses on developing and fine-tuning language models, requiring technical skills in machine learning and NLP. Data Scientists analyze data, build models, and generate insights. While both roles involve data and programming, Flexible Llm Training is specialized in AI model training, whereas Data Scientists have broader data analysis responsibilities.

What are the key skills and qualifications needed to thrive in flexible LLM training?

To excel as a Flexible LLM Training Specialist, you generally need a strong background in machine learning, natural language processing, and data engineering, often supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks such as PyTorch or TensorFlow, experience with distributed training systems, and knowledge of cloud computing platforms are typically required. Critical thinking, problem-solving, and adaptability are vital soft skills, as well as strong collaboration and communication abilities. These competencies ensure effective model development, efficient troubleshooting, and successful deployment of large language models in dynamic environments.
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Flexible Llm Training jobs near you
Infographic showing various Flexible Llm Training job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $77,988 per year, or $37.5 per hour.

LLM Training Engineer

Sciforium

San Francisco, CA • On-site

$155K - $220K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 8 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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