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Remote Llm Trainer Jobs (NOW HIRING)

CrowdStrike

AI Research Scientist (Remote)

Experience training generative models, with a strong command of LLM training fundamentals ... LI-Remote LI-RC1 Benefits of Working at CrowdStrike: * Market leader in compensation and equity ...

Alinia AI

Applied AI Research Intern

Demonstrated knowledge and practical experience in LLM training (Please note: this is a strict ... Hybrid or remote work, with preference for CET time zone * Collaborative culture: Small ...

Sigma Group

Tamil Translator (Remote) | Sigma AI

$45K - $58K/yr

... or LLM training * Strong attention to detail What will you do? Annotation - Audio/Video/Image ... remote , performed through an online platform available 24/7. This opportunity is offered for ...

Gnostech LLC

Software Engineer (US-Remote)

OR · On-site +1

Utilize AI-assisted development tools (e.g., LLM coding assistants, code analysis tools) to enhance ... Familiarity with the AI/ML lifecycle including data preparation, model training, evaluation ...

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 · 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 ...

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Remote Llm Trainer information

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How much do remote llm trainer jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for remote llm trainer in the United States is $36.91, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $52.88 per hour, depending on experience, location, and employer.

What is a remote LLM trainer?

Remote LLM Trainers are professionals who work from any location to help train large language models (LLMs) by providing high-quality data, evaluating model outputs, and refining model behavior. They may annotate data, review AI-generated content, or design prompts and tasks to improve the model's performance. These roles are crucial in ensuring that LLMs become more accurate, safe, and useful across various applications. Remote LLM Trainers often have backgrounds in language, linguistics, data science, or related fields and rely on digital tools to collaborate with AI development teams.

What does a remote LLM trainer do?

As a Remote LLM Trainer, your workday often involves creating, curating, and reviewing datasets, developing prompts, and evaluating large language model outputs for quality and safety. Much of your collaboration happens asynchronously through digital channels—such as project management tools, messaging platforms, and regular video meetings—with researchers, data scientists, and fellow trainers. You may also participate in feedback sessions to discuss model behavior and share insights on improving training methodologies. Adapting to rapidly evolving project requirements and maintaining clear communication are key to success in this distributed, fast-paced environment.

What are the key skills and qualifications needed to thrive as a remote LLM trainer?

To thrive as a Remote LLM Trainer, you need a deep understanding of machine learning, natural language processing, and large language models, typically supported by a degree in computer science or related fields. Experience with Python, deep learning frameworks like TensorFlow or PyTorch, and familiarity with annotation tools or data labeling platforms is essential. Strong communication, attention to detail, and the ability to work independently are standout soft skills in this role. These skills and qualities ensure accurate model training, effective collaboration with distributed teams, and the delivery of high-quality AI solutions.

What is the difference between Remote Llm Trainer vs Data Scientist?

AspectRemote Llm TrainerData Scientist
Required CredentialsBackground in AI, NLP, or machine learning; often a degree in computer science or related fieldDegree in computer science, statistics, or related fields; often certifications in data analysis or machine learning
Work EnvironmentRemote, collaborative teams developing and fine-tuning language modelsRemote or on-site, analyzing data, building models, and deriving insights
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting, and research organizations

While both roles involve working with data and machine learning, a Remote Llm Trainer specializes in training and refining language models, whereas a Data Scientist focuses on analyzing data, building predictive models, and deriving insights across various industries.

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Remote Llm Trainer jobs near you
Infographic showing various Remote Llm Trainer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 2% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $76,772 per year, or $36.9 per hour.

Senior Machine Learning Engineer, LLM Inference Optimization

Nebius

Palo Alto, CA • On-site, Remote

$144K - $189K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted yesterday

Job description

The role 

Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.

A Senior MLE owns substantial model and endpoint optimization projects end to end. They are deeply hands-on, can debug difficult serving problems independently, and can deliver measurable improvements without needing heavy supervision.

Your responsibilities: 

  • Own optimization work for specific model families, customer endpoints, or serving backends.

  • Run engine comparisons and recommend practical serving configurations for specific workloads.

  • Debug model quality or performance regressions during production rollouts.

  • Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token.

  • Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems.

  • Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery.

  • Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving.

  • Build reproducible benchmark harnesses for TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token.

  • Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers.

  • Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations.

Must-haves: 

  • Strong Python and PyTorch engineering skills.

  • Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems.

  • Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems.

  • Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving.

  • Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs.

  • Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams.

Nice-to-haves: 

  • Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related techniques.

  • Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods.

  • Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration.

  • CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role.

  • Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects.

Key employee benefits in the US:

  • Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.

  • 401(k) plan: Up to 4% company match with immediate vesting.

  • Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.

  • Remote work reimbursement: Up to $85/month for mobile and internet.

  • Disability & life insurance: Company-paid short-term, long-term and life insurance coverage.

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