Pareto.AI - Full Stack AI Engineer (Staff level)

• Seniority — Staff-level, 10+ years of software engineering experience with a track record of owning complex systems end-to-end. • Seniority • Software Engineer First — You are fundamentally a strong full-stack software engineer who has worked extensively in AI/ML contexts, not a data scientist who codes. You think in systems, architecture, and engineering tradeoffs. • Software Engineer First • System & Application Design — You take ambiguous business problems, reason through the architecture, and build the harness around an AI model that makes it reliable and scalable. You lead design discussions, not just participate in them. • System & Application Design • Production AI Systems — Significant experience building and shipping agentic workflows, multi-agent orchestration, human-in-the-loop (HITL) pipelines, and LLM-powered applications with measurable business outcomes. Hands-on with RAG, vector stores, semantic search, and multi-model LLM stacks. • Production AI Systems • Context Engineering — Battle-tested practices for dynamically supplying the right context for the right problem. You reason clearly about AI limitations and architect around them. • Context Engineering • Distributed Systems — Experience with distributed systems architecture in the context of AI or data platforms — not pure infrastructure. • Distributed Systems • Agentic Coding Proficiency — You use agentic coding tools to multiply your output, not pad it. You can 10x yourself without the work becoming slop. • Agentic Coding Proficiency • Quick Study — This role sits at the intersection of many complex technical domains. You ramp fast, develop enough understanding of adjacent systems to be effective, and don't need things explained twice. • Quick Study • Experience at an AI data company (Scale AI, Surge, Snorkel, Labelbox, or similar), particularly building synthetic data pipelines, eval environments, or task generation systems. • Experience building human data labeling interfaces, annotation workflows, or data collection pipelines, and working directly with their users to understand and improve the experience. • Familiarity with preference data and reward models used in AI model training (RLHF, RLVR, or similar). • Proficiency with specific tools and technologies in our stack: Python, TypeScript, AWS, GCP, Terraform, Temporal Cloud, containerization, LLM gateways, RAG frameworks, and data pipeline tooling.