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# Researchers in Generative AI

- **Company:** Pocket FM Corp
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Graph Database, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Machine Translation, Natural Language Processing, Performance Tuning, Recommender Systems, Tensorflow, Reinforcement Learning, Chatbots, Pytorch, Large Language Models, Deep Learning, Generative AI, Information Technology, Data Pipelines
- **Published:** September 26, 2026
- **Apply:** https://www.thejobnetwork.com/job/10c76ff9-2d03-4f83-9d21-522bcc37cfaa/member-of-technical-staff

## About the Role

We are looking for an experienced Researchers in Generative AI to work on a broad range of applied AI problems across Pocket Entertainment. This is not a role focused on a single model or modality. You could work on problems spanning LLMs, conversational AI, vision, multimodal models, content generation, adaptation and localization, AI agents, retrieval, personalization, and evaluation. You will take ideas from research and experimentation through to production, working on AI systems that directly impact how content is created, adapted, produced, and experienced by users globally. We are particularly interested in candidates who combine strong applied research skills with the ability to build and ship AI systems end-to-end., * Master's or PhD in Computer Science, Machine Learning, AI, NLP, Computer Vision, or a related field or equivalent depth gained through industry experience
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 * Strong Python skills and a solid foundation in modern deep learning and generative AI architectures, with hands-on experience training, fine-tuning, and shipping systems end-to-end
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 * Experience across one or more of: LLMs, conversational AI, multimodal AI, computer vision, machine translation, speech, or generative models with PyTorch or TensorFlow as your primary toolkit
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 * Hands-on experience with model adaptation techniques SFT, RLHF, DPO, LoRA, or reinforcement learning and a rigorous approach to evaluation, including synthetic data pipelines and LLM-as-a-judge systems
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 * Experience with retrieval and knowledge systems RAG, vector databases, knowledge graphs, or large-scale search and ranking
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 * Experience with long-form content generation, narrative AI, multilingual models, or culturally aware adaptation
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 * Background in personalization, recommendation systems, or consumer-facing AI products at scale, * Comfortable moving fluidly between research and production translating advances in AI into measurable improvements for real users



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