Full Stack AI Engineer (Staff level) at Pareto Ai

ABOUT US At Pareto.AI [Upgrade to PRO to see link] we’re on a mission to enable top talent around the world to participate in the development of cutting-edge AI models. In coming years, AI models will transform how we work and create thousands of new AI training jobs for skilled talent around the world. We’ve joined forces with top AI and crowd researchers at Anthropic, Character.AI, Imbue, Stanford, and University of Pennsylvania to build a fair and ethical platform for AI developers to collaborate with domain experts to train bespoke AI models. We're building an AI Strategy team that accelerates company functions through innovation and automation. We are the brakes on the race car of growth-induced manual operations — our goal is to ensure the company can scale revenue without linearly scaling its supporting headcount. We succeed by building innovative, effective, and dependable solutions that drive growth and keep human overhead in check. Responsibilities - Design Leadership — Own and lead the most complex system design discussions. Be the person the team comes to for architectural decisions. Negotiate, push back, and shape what gets built and what doesn't. - Feasibility & Scoping — Rapidly assess technical feasibility of AI product ideas before development begins. Produce one-page technical scoping documents that prevent scope creep and surface hidden risks upfront. - Technology & Frameworks — Define technology stacks, build reusable frameworks, and establish engineering guidelines that let the team move fast while maintaining quality standards. - Experimentation — Build prototypes with stakeholder alignment, get early signal on whether something will work, and kill or accelerate accordingly. - Cross-functional Collaboration — Partner closely with research, operations, and data teams to understand evolving needs and iterate quickly. Juggle multiple workstreams and make smart tradeoff decisions as priorities shift. - Excellence — Build systems that raise our execution muscle. Lead evaluation practices that measure AI application effectiveness. Qualifications - Seniority — Staff-level, 10+ years of software engineering experience with a track record of owning complex systems end-to-end. - 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. - 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. - 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. - 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. - Distributed Systems — Experience with distributed systems architecture in the context of AI or data platforms — not pure infrastructure. - Agentic Coding Proficiency — You use agentic coding tools to multiply your output, not pad it. You can 10x yourself without the work becoming slop. - 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. Nice to Have - 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.