LLM Algorithm Tech Lead

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 1,000,000 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.

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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.

We are seeking an experienced LLM Algorithm Tech Lead to drive the design of AI intelligence architecture and lead the end-to-end application of large language models in real-world product environments. This role focuses on reasoning capability design, intelligent behavior frameworks, knowledge/RAG pipelines, inference optimization, and cross-team engineering collaboration.

You will architect the core intelligence layer behind next-generation AI products and ensure LLM capabilities can be reliably, safely, and efficiently applied in production.

1. Intelligence Architecture Design

• Design LLM reasoning frameworks: task decomposition, structured reasoning, and decision logic

• Architect intelligent behavior modules including context handling, memory mechanisms, and proactive insights

• Define the capability roadmap and build modular, extensible intelligence components

1. Applied LLM System Development & Production Integration

• Lead the full lifecycle of LLM-powered features: requirement → design → prototyping → production

• Optimize model outputs for reliability, consistency, controllability, and domain suitability

• Implement hallucination mitigation strategies via prompting, constraints, and structured reasoning

1. Knowledge Integration & RAG Pipelines

• Build multi-source retrieval pipelines for structured and unstructured data

• Own chunking, embedding, reranking, and context-injection strategies

• Ensure RAG-enhanced reasoning is stable and production-grade

1. Inference Optimization & Model Strategy

• Evaluate and select LLMs (OpenAI, Claude, DeepSeek, Qwen, Llama, Gemini, Mistral)

• Build model-routing logic based on latency, cost, performance, and task type

• Work with engineering to optimize runtime, memory usage, and system availability

1. Cross-Functional Collaboration

• Partner with engineering, product, and data teams to deliver high-quality LLM features

• Define evaluation standards, monitoring metrics, fallback strategies, and safety constraints

• Lead issue investigation and optimization in production systems

• 5+ years of experience in NLP/LLM/AI development

• Strong expertise in prompt engineering, LLM reasoning design, and applied ML architecture

• Experience deploying LLM-powered features in production

• Familiarity with RAG/knowledge pipelines and hallucination mitigation

• Strong system thinking and ability to architect complex workflows

• Excellent communication and cross-functional collaboration skills

• Ability to work effectively in global, distributed teams

• Experience with personalization, memory systems, or agent workflows

• Familiarity with LLM eval pipelines and safety mechanisms

• Experience leading small technical teams

• Experience with large-model algorithm design or foundational model capability development (architecture, distillation, quantization, optimization)

• Experience in multi-modal reasoning or large-scale systems

• Architect intelligence for next-generation AI products

• High autonomy in technical direction

• Work with top AI engineers across countries

• Influence global-scale intelligent systems