1 to 25 of 2,553 Remote LLM Jobs

world's largest law firms. In this role, you will leverage your deep expertise in machine learning, natural language processing, and large language model (LLM) agents to push the boundaries of legal technology. You'll be collaborating closely with data scientists, software engineers, and product managers to integrate advanced … NLP techniques into our products. What You'll Do Innovative NLP Research: Research, evaluate, and implement state-of-the-art NLP methodologies and large language model approaches to drive product innovation and develop new functionalities. LLM Agents & Multi-Agent Systems: Design, develop, and deploy LLM agents and multi-agent systems … Continuous Improvement: Proactive in identifying problems, performance bottlenecks, and areas for improvement while taking pride in building and operating scalable, reliable, and secure systems. LLM Agents & Multi-Agent Systems: Proven experience in designing, deploying, and scaling large language model (LLM) agents and multi-agent systems to enhance NLP capabilities and More ❯

to set job alerts for “Principal Research Scientist” roles. Senior Research Scientist, Evidence Synthesis London, England, United Kingdom 6 months ago Senior Research Scientist (LLM post training) We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. #J More ❯

We are seeking an experienced AI Developer with a strong background in Large Language Models (LLMs) to join our AI team. The ideal candidate will have expertise in retraining and fine-tuning LLMs using proprietary datasets to build a conversational More ❯

space (LLMs) & share your experience with optimisation techniques such as quantization and pruning to minimise training & inference requirements for models. Design and implement effective LLM-tooling, finetuning & response evaluation strategies. Guide teams on the latest AI solution development practices and capabilities, and accordingly refine and improve our model development lifecycle More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

and fine-tuning AI models. AI-Driven Data Processing: Develop models for structuring, classifying, and extracting insights from clinical, operational, and patient-reported data. LLM Integration & Prompt Engineering: Designing structured prompts, tool usage, and system messages Natural Language Processing (NLP): Develop tools to extract, summarize, and contextualize medical information from More ❯

communication skills, both with teams and in client-facing engagements Interested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agents Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries ( Pytorch, Tensorflow) from early More ❯

of GDPR-compliance and data governance in the context of machine learning. Experience in insurance or financial services. Familiarity with OpenAI, GPT, or other LLM technologies. Infrastructure management, including monitoring, backup, and disaster recovery. What We Will Offer You Recognised and rewarded for a job well done, we have a More ❯

learning) to address specific business challenges. • Work with Large Language Models (LLMs): fine-tuning, prompt engineering, developing Retrieval-Augmented Generation (RAG) systems, and integrating LLM capabilities into applications via APIs. • Process, clean, and analyze large datasets to prepare them for model training and evaluation. • Develop APIs and services to integrate More ❯

lifecycle management Experience with common ML libraries and frameworks such as TensorFlow, PyTorch, Keras, Scikit-learn Familiarity with timely engineering and large language models (LLM) is a plus Proficiency in multiple programming languages Understanding of containerization technologies like Docker and orchestration tools like Airflow We've got your back... We More ❯

governance and transparency. Perform and guide model retraining, performance monitoring, and updates for complex production models (e.g., time series, anomaly detection, natural language processing, LLM-based text summarization), proactively addressing performance gaps. Collaborate closely with domain experts to capture critical business requirements, and lead efforts to gather, transform, and prepare More ❯

performance Comfortable with PyTorch Knowledge of Transformer-based models Knowledge of Large Language Models (LLMs) Proven experience of having delivered successful Computer Vision or LLM projects into production Strong understanding of software development fundamentals, in particular deploying models to production and how to set up pipelines Demonstrate expertise in deep More ❯

. Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Experience with LLM application design and deployment. Strong software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools More ❯

and business intelligence tools to derive valuable insights from data. Promote data-driven decision-making across the organisation. Operationalise ML, GenAI & RAG - strengthen our LLM-based assistant so colleagues can ask plain-English questions and get clear answers Implement data quality management processes to identify and rectify data errors or More ❯

MLX) Popular classification and regression techniques Unsupervised learning & matrix factorisation algorithms Natural Language Processing (NLP) and document processing Generative AI (open and closed source) - LLM, RAG, Fine Tuning Performing model selection and evaluation Knowledge of cloud (AWS/Azure/GCP) Business skills Working directly with clients, users and data More ❯

training robotic policies, training from simulation/Digital Twins. Proficiency in deploying AI models and optimizing inference using TensorRT, ONNX Runtime, Triton, or TensorRT-LLM is a plus. Proven experience implementing and optimizing workloads with CUDA and Nsight Tools. Experience with high performance networking technologies, e.g. DPDK, DOCA, RMDA, RoCEv2 More ❯