The ad says“Experience with LLM Ops and tools (e.g., vector databases, RAG, prompt ops)”
The ad says“Proficiency in React/Node/Typescript and Python”
The ad says“Experience with LLM Ops and tools (e.g., vector databases, RAG, prompt ops)”
The ad says“Proficiency in React/Node/Typescript and Python”
The ad says“Experience with LLM Ops and tools (e.g., vector databases, RAG, prompt ops)”
The ad says“Proficiency in React/Node/Typescript and Python”
The ad says“Proficiency in React/Node/Typescript and Python”
The ad says“Experience with LLM Ops and tools (e.g., vector databases, RAG, prompt ops)”
As an AI Product Engineer at Speak, you’ll play a pivotal role in developing the future of language learning and creating the most effective path to language fluency
Your primary responsibility will be developing innovative product experiences powered by Language Learning Models (LLMs) and deploying them to millions of users
Your tasks will span the full stack and beyond, from evaluating models and testing prompts to developing and iterating on various product features such as our conversational onboarding and lesson experiences, grammatical assessment and feedback, and personalized lessons
We’re constantly pushing the boundaries of what LLMs can do to provide an exceptional and unparalleled language learning experience to users in over 30 countries worldwide
Developing and deploying LLM-powered language learning products across the full stack, as well as enhancing the quality and performance of existing AI-powered features within Speak
Collaborating cross-functionally with other Engineering teams, Applied ML, Product, Design, and Content
Refining our process for building LLM apps, including best practices for prompting, experimentation/evaluation, LLM Ops, measuring quality and performance, etc
Scaling existing product features to many more users and languages
Benefits
Competitive salary and equity
Comprehensive medical, dental, and vision insurance
Unlimited PTO
Parental leave
401(k)
Monthly wellness stipend
Monthly language learning stipend
Preferred experience
Strong product intuition — the ability to think broadly and cross-functionally about innovative LLM-powered capabilities and product experiences
Ability to work independently and build at a high velocity
Proficiency in React/Node/Typescript and Python
You may have participated in LLM hackathons, developed several side projects, experimented with different types of models, and use ChatGPT daily
Interest in building with LLMs
3-5+ years of experience in full stack/backend, product-focused software engineering
You have real-world experience developing and deploying LLM apps and a strong understanding, gained through experience, of what works and what doesn’t
A keen intuition for improving performance and output quality of LLM systems
Experience with LLM Ops and tools (e.g., vector databases, RAG, prompt ops)
