Technical Builder [Senior AI Full-Stack Engineer]

Full-time

Job Description

Who we are:

AI is the new electricity: Just as electricity transformed numerous industries starting 100 years ago, AI is now poised to do the same. AI Fund is a venture studio founded by Dr. Andrew Ng in 2017. Our portfolio companies use AI technology to build applications across numerous industry sectors. The AI Fund team combines their experiences as AI pioneers, entrepreneurs, venture capitalists, investors, and operators. We are backed by a $370-million dollar fund from top-tier global corporations and VC firms. Our purpose is to build AI companies that move humanity forward

What We’re Looking For:

For the Technical Builder role, we are seeking a Senior AI Full-Stack Engineer with a strong architecture background, fluent in full-stack development and proficient in designing scalable AI-driven systems. You should be deeply experienced in architecting complex applications, leveraging modern Generative AI frameworks, and comfortable leading the design of robust, production-grade architectures. The ideal candidate thrives in fast-paced environments where the goal is to quickly build and iterate on full-stack applications leveraging cutting-edge AI techniques, as well as guiding technical vision and defining architectural patterns.

You're passionate about staying ahead of the curve in AI—constantly exploring new tools, frameworks, and best practices. You’ll collaborate closely with world-class founders, product thinkers, and engineers to bring experimental ideas to life through functional prototypes.

This role puts you in the front row of where AI is heading, working with technologies that are helping shape the future. If you’re driven by curiosity, innovation, and the thrill of building what’s next—this is the role for you.

This is a full-time hybrid position for candidates based in San Francisco, Bay Area.

What you will do:

Drive rapid engineering efforts to build full-stack applications using GenAI tools, enabling deep exploration of product ideas, user experiences, and technical feasibility

Apply GenAI building blocks (prompt engineering, graphDBs, vectorDBs, agentic frameworks, evals, and guardrails) to real-world development

Lead architecture design and technical planning for scalable AI applications, ensuring system reliability, maintainability, and performance

Make key decisions around infrastructure and platform choices, balancing short-term prototyping needs with long-term scalability

Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new conceptsUse AI-assisted coding tools (e.g., Cursor, Windsurf, GitHub Copilot) to enhance productivity and speed of iteration

Gather user feedback and iterate quickly based on insights to improve usability and effectivenessDeploy and manage applications on cloud infrastructure (e.g., AWS, GCP, Supabase)Build and integrate APIs and third-party services

Participate in architecture discussions and technical planningIdentify and troubleshoot issues across the stack

Contribute to improving development processes, tools, and team practices

Stay current with industry trends and emerging technologies

What you must bring:

7+ years of experience in software engineering with significant architecture responsibilities, including at least 3 years developing AI-powered applications

Demonstrated experience building applications that incorporate Generative AI

Proficiency with GenAI tools and frameworks: prompt engineering, Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, Weaviate), agentic platforms (e.g., LangGraph, CrewAI), LLM evals, and guardrailsExperience with front-end technologies like JavaScript, HTML, CSS, and modern frameworksStrong back-end skills, particularly with Python

Familiarity with SQL and NoSQL databases (PostgreSQL, MongoDB, etc.)

Hands-on experience using AI-assisted coding tools (e.g., Cursor, Claude Code) and understanding of best practices for effective useProven track record of architecting, implementing, and deploying scalable and maintainable AI-powered systems

Expertise in designing end-to-end system architectures involving front-end, back-end, APIs, and AI/ML components

Deep curiosity and a fast-learning mindset—especially around emerging trends in AI

Ability to write scrappy, disposable code for fast prototyping, and clean code for scalable products

Strong communication skills and ability to work collaboratively across disciplines