2 months ago
Remote, United States or San Francisco, CA, USAEntry Level / Mid Level / Senior / Staff+
Base Salary
$110k - $330k/yr
Responsibilities
- Build and ship AI-native products and product features end-to-end.
- Design and optimize LLM pipelines, RAG systems, autonomous agents, and agentic workflows.
- Integrate AI capabilities into crypto-native infrastructure, protocols, or consumer products.
- Work on model evaluation, fine-tuning, and inference optimization at scale.
- Collaborate with small, high-ownership teams to deliver quickly.
Requirements
- Experience or familiarity with full-stack AI work spanning model integration and product-facing features.
- Experience building autonomous AI agents, agentic workflows, or agent-to-agent systems.
- Familiarity with agentic payments, machine-to-machine transactions, or AI-native economic primitives.
- Experience building and deploying AI systems, LLM-powered applications, or ML pipelines in production.
- Experience with foundation models, fine-tuning, RAG pipelines, agents, or inference optimization.
- Engineers passionate about AI and/or crypto are encouraged to apply even if they do not match every listed qualification.
Benefits
- Confidential matching with aligned Dragonfly portfolio companies and warm introductions when mutual interest exists.
- Potential consideration for future opportunities if there is no immediate match.
- Compensation varies by seniority, location, and hiring company, with listed ranges from $110,000 to $330,000.
Categories
About Dragonfly
DragonFly (dragonflymax.com) builds a cloud platform used by K–12 and scholastic athletic programs to manage eligibility, digital forms, compliance workflows, rosters, scheduling, communications, and payments. It provides athletic trainers tools for documenting injuries and care, and gives administrators centralized oversight across teams and seasons. The company sells its software as a subscription to schools, districts, and state associations, with integrated services for collections and reporting.
