Remote positions
Browse remote Hugging Face positions at AI companies — LLM fine-tuning, Transformers library engineering, and NLP roles working with open-source models.
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Posted 6 months ago
Applications closedSentinelFlow AI
Remote
We're a pharmaceutical AI company. We build knowledge graphs connecting drugs, targets, diseases, clinical trials, patents and scientific literature —...
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We are a Gulf retail bank. We want to deploy an AI chatbot that handles Tier-1 customer enquiries — balance checks, transaction history, card blocking...
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We're a small but fast-moving AI studio and we're looking for a Senior LLM Engineer to own the core of our language model stack. You'll work directly...
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Posted 7 months ago
Applications closedCyberSense AI
Remote
We build contract analytics software. Our current NER pipeline was fine-tuned 2 years ago on a limited dataset and it's not keeping up with contract c...
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Posted 8 months ago
Applications closedAxiom Labs
Remote
We run a SaaS platform for e-commerce merchants. We want to build a fine-tuned support bot that handles 80% of incoming tickets automatically — refund...
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Posted 9 months ago
Applications closedMediqAI
Remote
Consumer goods company. 4 brands. We monitor Twitter, Instagram, TikTok comments and Google reviews — currently manually, by two people who read throu...
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Role guide
Hugging Face has become the central hub for open-source LLM development. Remote Hugging Face jobs involve fine-tuning and deploying models from the Hub, building NLP pipelines with the Transformers library, and contributing to inference optimisation. Engineers with Hugging Face experience are at the core of the open-source AI movement.
What Hugging Face roles are available remotely?
LLM fine-tuning engineers, NLP developers, and ML research engineers who work primarily with the Hugging Face ecosystem. Most roles require PyTorch alongside Transformers library experience.
Do I need to know LoRA for Hugging Face remote jobs?
For fine-tuning roles, yes. LoRA and QLoRA (via the PEFT library) have become standard for parameter-efficient fine-tuning of large models. Full fine-tuning roles are rarer and typically require more compute resources.
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