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Remote Llm Researcher Jobs (NOW HIRING)

Alinia AI

Applied AI Research Intern

Co-founders Ari and Carlos come from leading ML platform at Twitter and LLM governance at Hugging ... Hybrid or remote work, with preference for CET time zone * Collaborative culture: Small ...

USM

JB061755 - AI Prompt Engineer - Remote

This is for a healthcare research client and looking for candidates who are located preferably in ... LLM, LLM instructions * Build and Test AI workflows * AI Experience - Perplexity, ChatGPT or ...

NextDeavor Inc.

Senior AI Researcher

New York, NY · On-site +1

$150K - $220K/yr

New York preference but open to remote; you must work EST hours. Here's How You'll Make an Impact ... Experience with agentic LLM systems: tool use, multi-step reasoning, planning, or long-horizon ...

Lucas James Talent Partners

AI Research Engineer - NLP

Evanston, IL · On-site +1

$100K - $120K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

This is a REMOTE opportunity. The AI Research Engineer advances Digital Safety mission by designing ... Conducts literature and open-source reviews in NLP or LLM Designs, plans, and creates NLP or LLMs ...

UL Research Institutes

Lead Research Scientist in AI

This isa REMOTE opportunity. The Lead Research Scientist in AI directs advanced research in natural ... This role leadsresearchin NLP and LLM safety including literature surveys, experimental design, AI ...

Higharc

Research Engineer

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Design end-to-end LLM training and fine-tuning pipelines tailored to construction domains ... Working at Higharc Higharc has been remote first since our founding in 2018. We offer flexible ...

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Remote Llm Researcher information

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$30K

$113.1K

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How much do remote llm researcher jobs pay per year?

As of Aug 16, 2026, the average yearly pay for remote llm researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote LLM researcher, and why are they important?

To thrive as a Remote LLM Researcher, you need a strong background in machine learning, natural language processing, and deep learning, typically supported by an advanced degree in computer science or a related field. Familiarity with frameworks like PyTorch or TensorFlow, experience working with large language models (LLMs), and knowledge of distributed computing tools are commonly required. Outstanding problem-solving abilities, communication skills, and the ability to work independently are essential soft skills for remote collaboration and research innovation. These skills enable effective development, evaluation, and deployment of advanced language models in a distributed team environment.

How do remote LLM researchers typically collaborate with cross-functional teams given their distributed work environment?

Remote LLM Researchers often work closely with data scientists, machine learning engineers, and product managers via virtual collaboration tools such as Slack, Zoom, and GitHub. Regular meetings, shared documentation, and project management platforms help maintain clear communication and alignment on research goals. Being proactive in sharing updates, seeking feedback, and participating in code reviews is essential for seamless teamwork. This collaborative approach ensures research findings can be effectively integrated into products and services, despite the physical distance.

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

AspectRemote Llm ResearcherRemote Data Scientist
CredentialsAdvanced degrees in AI, NLP, or related fields; research experienceDegree in Data Science, Statistics, or Computer Science; often includes certifications
Work EnvironmentResearch-focused, often in AI labs or tech companies, remote options availableData analysis, modeling, and visualization tasks, remote or on-site
Industry UsageAI research, NLP development, machine learning innovationBusiness analytics, predictive modeling, data-driven decision making

Remote Llm Researchers focus on developing and improving large language models through research and experimentation, often in AI labs. Remote Data Scientists analyze data to generate insights and build predictive models. While both roles require strong technical skills, Remote Llm Researchers are more research-oriented, whereas Remote Data Scientists focus on applying data techniques to solve business problems.

What is a remote LLM researcher?

A Remote LLM Researcher is a professional who studies and develops large language models (LLMs), such as GPT or BERT, while working from a location outside of a traditional office setting. Their work typically involves conducting experiments, analyzing data, improving model architectures, and publishing findings in the field of natural language processing (NLP). Remote LLM Researchers often collaborate with colleagues online, use cloud-based computing resources, and contribute to advancements in AI language technologies. This role requires strong programming skills, a background in machine learning, and the ability to work independently in a distributed team environment.
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Remote Llm Researcher jobs near you
Infographic showing various Remote Llm Researcher job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, 4% Part Time, and 9% Contract. Highlights an 100% Remote job distribution, with an average salary of $113,102 per year, or $54.4 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

Posted 24 days ago

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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