Applied AI Engineer
We’re a venture capital firm with an in-house venture studio, built to turn bold ideas into venture-scale startups. Beyond direct investments, we operate at zero-to-one; teaming up with operators, engineers, designers, and domain experts to validate real-world problems and build from scratch.
Over the past three years, Fund I has backed 70+ startups; not just with capital, but with hands-on support to engineer real wins across BioHealth, B2B SaaS, AI, and beyond.
We’re looking for a Applied AI Engineer with strong technical skills, product instincts, and the ability to dive into complex system design. You’ll be hands-on with multiple early-stage projects across our venture studio, helping us validate opportunities and bring AI-driven products to life.
What you’ll do
Build core modules: Develop and maintain AI-powered features and systems across our in-house venture projects.
Optimize AI systems: Improve performance through prompt engineering, retrieval-augmented generation (RAG), tool design, and scalable agent architectures.
Prototype rapidly: Validate new product ideas by shipping quick, testable prototypes that inform venture direction.
Engineer evaluation pipelines: Design automated evaluation systems to measure and improve model and product reliability.
Work full-stack: Contribute across the stack — from front-end evaluation dashboards and user-facing tools to backend systems (databases, caching, microservices).
Operate in hyperdrive: Switch contexts across multiple ventures, iterating quickly and collaborating with cross-functional founders and teams.
What we’re looking for
Strong programming fundamentals and a bias for clean, high-quality code.
Proficiency in Python and familiarity with AI/ML frameworks (e.g. LangChain, Hugging Face, PyTorch, TensorFlow, OpenAI APIs).
Solid backend knowledge: databases, caching, message queues, microservices.
Comfort with frontend prototyping for internal tools, dashboards, or MVPs.
Proven ability to work on zero-to-one products, ideally at startups or studios.
Strong written and verbal communication; our team is fully remote.
Experience with evaluation pipelines, vector databases, or applied ML in production.