Member of Technical Staff, Full-Stack/Front-end

TypeSafe AI•San Francisco, CA

11d•$150,000 - $250,000•Onsite

About The Position

TypeSafe AI is an AI lab building machine-native intelligence infrastructure for automation. The company combines the intelligence of large language models with the efficiency and reliability of code to create System One Models. Based in San Francisco, TypeSafe AI launched its first public model, Jev, and is developing a general frontier model focused on real-world reliability, decision-making, and autonomy in production. Its small team includes people from OpenAI, Google Brain, and Meta/FAIR and is backed by investors. The role is for a scrappy engineer who can rapidly build data-tooling interfaces and advance model intelligence through interfaces, datasets, and evaluation tooling. The work combines product sense, data science, and engineering to help make models reliable in production. The primary technology stack is Python, with TypeScript, Next.js, and Tailwind CSS for frontend development and Kubernetes for orchestration. Developers may use tools they find helpful, including Claude Code and Cursor. The position is fully in person at the company’s San Francisco office near Embarcadero station. TypeSafe describes itself as a small, flat, close-knit team that values excellence, craftsmanship, collaboration, emotional honesty, and kindness.

Requirements

Solid full-stack engineering fundamentals and strong coding ability.
Product intuition.
High attention to detail and a strong bar for quality.
Interest in how systems are used in the real world, beyond the code.
A scrappy, resourceful approach and comfort thriving in ambiguous problem spaces.
Hands-on experience implementing LLMs and an understanding of their capabilities and limitations.
Does not trust LLMs blindly and examines the reality of model generations.

Responsibilities

Create high-leverage datasets, products, and user interfaces that unlock new capabilities and use cases for the model.
Build internal data-tooling and evaluation interfaces for evaluation, debugging, and data inspection.
Develop and own evaluation frameworks to measure quality, reliability, and emergent characteristics across model iterations.
Run rigorous analyses and experiments to understand how data, training choices, and targeted interventions affect model behavior.
Turn raw model outputs and data into clear, inspectable user interfaces, owning a domain end-to-end with craft and judgment.
Partner closely with product managers and customers to translate real-world needs into concrete model and system improvements.
Continuously iterate, learn, and co-discover techniques for building exceptional, trustworthy AI products.