Member of Technical Staff

Job DescriptionPocket Entertainment is building the world’s largest AI Entertainment Platform, reimagining how stories are created, distributed, localized, and experienced globally. AI is core to our technology and product ecosystem. We are building across Generative AI, LLMs, multimodal AI, voice, conversational systems, localization, content intelligence, and agentic workflows to transform how entertainment is created and consumed.\n\nAbout the role \n\nWe 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.\n\nWhat you'll work on \n\nYou'll work at the frontier taking research ideas into production, building systems that are both technically rigorous and genuinely useful at scale. The best AI products emerge when research, engineering, and creative judgment converge.\n\n Research, prototype, and ship state-of-the-art generative AI and multimodal systems from proof of concept to production\n Build for long-form content at scale: narrative reasoning, character consistency, story structure, and cross-lingual adaptation that preserves meaning and voice\n Develop conversational AI and agentic systems spanning multi-turn dialogue, memory, personalization, tool use, and complex workflow orchestration\n Design retrieval and knowledge systems using RAG, semantic search, vector databases, and knowledge graphs\n Train and fine-tune models using SFT, RLHF, DPO, LoRA, and reinforcement learning and build the evaluation frameworks to measure what matters\n Optimize for production: latency, throughput, reliability, and inference cost, while continuously improving quality from real-world data and model failures\n Collaborate across research, engineering, product, design, and creative and contribute to publications at leading AI/ML venues where the work warrants it\n\n\nAreas you may work across \n\n\n Generative AI & LLMs: Long-form generation, reasoning, fine-tuning, and evaluation\n Conversational AI & agents: AI characters, memory, tool use, and workflow orchestration\n Multimodal & vision: Image and video understanding, generation, and creative workflows\n Localization & adaptation: Multilingual LLMs, cultural adaptation, and quality evaluation\n AI content & storytelling: Narrative generation, creator copilots, and story intelligence\n Retrieval & knowledge systems: RAG, embeddings, knowledge graphs, and large-scale retrieval Voice & audio AI: Expressive TTS, speech understanding, and audio intelligence\n\n\nWhat we're looking for\n\n\n Master's or PhD in Computer Science, Machine Learning, AI, NLP, Computer Vision, or a related field or equivalent depth gained through industry experience\n 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\n 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\n 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\n Experience with retrieval and knowledge systems RAG, vector databases, knowledge graphs, or large-scale search and ranking\n Experience with long-form content generation, narrative AI, multilingual models, or culturally aware adaptation\n Background in personalization, recommendation systems, or consumer-facing AI products at scale\n Comfortable moving fluidly between research and production translating advances in AI into measurable improvements for real users\n Publications at leading AI/ML/NLP/CV conferences are a strong plus\n\n