LLM Algorithm Tech Lead – Applied Large Language Model Systems

Plaud
San Francisco, CA$230k – $300kPosted 12 March 2026

Job Description

LLM Algorithm Tech Lead – Applied Large Language Model Systems About Plaud Inc. Plaud is building the world's most trusted AI work companion for professionals to elevate productivity and performance through note-taking solutions, loved by over 1,500,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud is building the next-generation intelligence infrastructure and interfaces to capture, extract, and utilize what you say, hear, see, and think. Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human–AI intelligence through a hardware–software combination. With SOC 2, HIPAA, GDPR, ISO27001, ISO27701, and EN18031 compliance, Plaud is committed to the highest standards of data security and privacy protection. To learn more about Plaud, please visit http://www.plaud.ai/https://www.Plaud.ai and follow along on Instagram https://www.instagram.com/plaud_official/, https://twitter.com/PLAUDAI%22HYPERLINK%20%22https:/twitter.com/PLAUDAI%22%20%5C%22HYPERLINK%20%22https:/twitter.com/PLAUDAI%22%20%5CX https://twitter.com/PLAUDAI, Facebook https://www.facebook.com/plaudai, LinkedIn https://www.linkedin.com/company/plaudai/?viewAsMember=true, and YouTube https://www.youtube.com/@PLAUDAI Why You Should Join Us Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think. - Plaud is a bootstrapped, skyrocketing, profitable company with a $250M revenue run rate achieved in just three years. - Define the next-gen paradigm for human-AI interaction. - Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion. - Work with passionate teammates who value innovation, collaboration, and customer success. - Grow your career in a culture that champions continuous learning and fast career development. - Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere. What You Will Do - Intelligence Architecture Development - Design structured reasoning pipelines, planning flows, chain-of-thought workflows - Build capability primitives such as memory, personalization, proactive insights - Develop modular and reusable intelligence components - Applied LLM Features & Production Integration - Lead the design and deployment of LLM-based product functionality - Ensure output reliability, consistency, safety, and user-centric alignment - Apply prompting, constraints, and reasoning structures to reduce hallucination - Retrieval-Augmented Generation (RAG) - Build and optimize multi-hop, multi-source retrieval pipelines - Implement chunking, indexing, reranking, and retrieval evaluation - Ensure RAG improves factuality and reduces error rates - Model Strategy & Inference Optimization - Select appropriate model families based on capability and cost constraints - Develop routing logic for hybrid model usage - Collaborate with engineering to optimize latency, cost, and scalability - Cross-Functional Collaboration - Work closely with product, engineering, and data stakeholders - Define evaluation frameworks and monitoring pipelines - Troubleshoot production issues and lead algorithm-level improvements Skills, Qualifications and Experience We Look for - 5–10 years of experience in LLM/NLP/AI - Strong prompt engineering and reasoning design skills - Proven ability to deliver LLM-powered features into production - Experience with RAG and knowledge-enhanced reasoning - Strong architectural thinking and system design skills - Strong communication and leadership capabilities - Knowledge of memory systems or personalization engines - Experience building eval frameworks or safety systems - Experience leading technical teams - Experience in foundational model algorithm design or efficiency optimization - Exposure to multimodal or large-scale distributed systems What We Offer - Founding ... (truncated, view full listing at source)
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