Full Stack AI Engineer – Staff Level

Job not on LinkedIn

🕒 April 2

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🤖 AI Engineer

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Pareto.AI

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2020

🤖 Artificial Intelligence

🤝 B2B

Artificial Intelligence • B2B

Pareto. AI is a human-centered partner for frontier AI research and development that orchestrates networks of domain specialists to produce context-rich, high-quality data, run experiments, and evaluate models. The company provides managed, on-demand expert teams (from PhDs and medical doctors to technical specialists) to help AI organizations scale expert-only capabilities, accelerate research, and deliver robust, high-fidelity datasets and evaluations.

📋 Description

• 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. • 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. • Define technology stacks, build reusable frameworks, and establish engineering guidelines that let the team move fast while maintaining quality standards. • Build prototypes with stakeholder alignment, get early signal on whether something will work, and kill or accelerate accordingly. • 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. • Build systems that raise our execution muscle. Lead evaluation practices that measure AI application effectiveness.

🎯 Requirements

• Staff-level, 10+ years of software engineering experience with a track record of owning complex systems end-to-end. • 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. • 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. • 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. • Battle-tested practices for dynamically supplying the right context for the right problem. You reason clearly about AI limitations and architect around them. • Experience with distributed systems architecture in the context of AI or data platforms — not pure infrastructure. • You use agentic coding tools to multiply your output, not pad it. You can 10x yourself without the work becoming slop. • 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.