You have 7+ years of experience, with at least 4 of those years dedicated to building distributed, scalable web applications. You possess strong communication skills, problem-solving abilities, and a drive to deliver outstanding customer experiences for both external users and internal stakeholders.
You are familiar with underlying technologies like transformer networks, attention mechanisms, and how they contribute to models’ abilities to generate coherent responses, generate function calls, and perform other language tasks.
You have worked with large language models (LLMs) to perform complex tasks in production environments. You’re well-versed in advanced prompt techniques for LLM grounding, such as chain-of-thought, static few-shot examples, and dynamic few-shot examples. You also have experience fine-tuning LLMs to meet specific needs, focusing on training data optimization and bias mitigation.
You are proficient in deploying evaluation frameworks for LLMs, with a focus on performance, reliability, and bias assessment.
You have experience with Retrieval-Augmented Generation (RAG) systems and understand how to optimize knowledge retrieval for improved model accuracy and speed. You have a solid understanding of different indexing and chunking strategies based on the system’s data and goals, as well as semantic search and vector databases, and how they differ from traditional retrieval methods and databases.
You have a proven track record of working through the full lifecycle of building, testing, deploying, and scaling LLM architectures.
You are an expert at identifying and documenting trade-offs made during the development process.
You have strong experience building with cloud infrastructure technologies.
You love shipping to customers. You’ll be on a team focused on understanding customers' needs and translating those needs from specifications into functional, production-ready code. You know how to balance speed versus quality to support the features you build.
Things You’ll Do
You understand that AI-based applications thrive on data-driven feedback loops, which will be central to any system you develop. These loops will capture and instrument user data, synthesize core use cases, and implement/test strategies with LLMs to enhance performance.
You will be responsible for integrating LLMs into software products, setting up the necessary infrastructure to ensure performance, scalability, and reliability.
You will explore new and divergent LLM systems that can create measurable improvements over current implementations.
You will design and implement prompting strategies for LLMs to help users create new automations, making the process more intuitive and efficient.
You will design and develop comprehensive evaluation suites to assess model performance and reliability.
You will monitor the performance and health of AI systems, proactively detecting and addressing issues such as system failures and performance degradation.
Depending on your experience, you may implement LLM-assisted retrieval algorithms to provide accurate answers over large datasets.
You will collaborate with Data and cross-functional teams to refine and deploy LLM-based features.
Sign in with Google
