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Shuttlebase is building self-evolving websites powered by generative AI, large-scale data pipelines, and continuous experimentation. The company merges AI-driven optimization with web infrastructure to help sites rebuild themselves automatically to sell more, convert better, and grow faster. This is a well-funded, very early-stage startup offering the autonomy of a founder role with top-tier investor backing.
As Senior Fullstack Engineer, you will be hands-on across the entire stack with significant autonomy and ownership. Your responsibilities include:
- Architect and build scalable systems processing tens of millions of web events per day, spanning backend APIs to frontend dashboards
- Design large-scale data infrastructure that analyzes how websites are built, explains performance characteristics, and identifies optimization opportunities
- Train and integrate large language models into production to automatically hypothesize, generate, and validate website variants
- Shape engineering culture, practices, and technical direction — your decisions will influence the company's tech stack, architecture, and team processes
- Experiment, prototype, and iterate rapidly, blending research, product development, AI, and scale
- Work directly with the founding team in a zero-to-one environment where impact is immediate and visible
This role is not about maintaining a standard SaaS application; it's about building an AI engine that fundamentally redefines how digital products are created and optimized.
The company culture emphasizes curiosity and growth, radical ownership of outcomes, radical transparency, shipping speed, and trust. You'll work in an office-first environment (4 days/week) in Midtown Manhattan with direct access to founders.
REQUIREMENTS:
- 5+ years of fullstack software engineering experience
- Proficiency in Python and JavaScript; AWS experience preferred but not required
- Experience leading teams or driving major projects
- Background in Computer Science, Electrical Engineering, Mathematics, or equivalent hands-on technical mastery
- Demonstrated enthusiasm for AI and desire to build with LLMs in production
- Bonus: startup experience, experience training or deploying ML models in production
