Applied AI Engineer

ABOUT US At Pareto.AI http://Pareto.AI, 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. You'll be joining an applied AI team that makes the company run better as it grows. As the business scales, manual work piles up — this team steps in and fixes that, building agentic workflows, automation pipelines, and smart systems that handle the complexity so people don't have to. You succeed when the business can grow without things breaking or slowing down. Responsibilities - Design and build the pipelines that generate synthetic tasks and evaluation environments for AI model training — this is the factory floor of AI development, producing training fuel for next-generation models, not the models themselves - Architect the workflows where AI and humans work together in the loop — deciding what gets automated, what requires human intervention, how state is preserved across handoffs, and how the whole system stays reliable at scale - Own and lead the most complex system design discussions — produce one-page technical scoping documents that surface hidden risks before development begins, define technology stacks, and establish engineering guidelines that let the team move fast without breaking things - Rapidly assess whether a technical idea is worth building — get early signal, align stakeholders, and kill or accelerate accordingly - Partner closely with research, operations, and data teams — juggle multiple workstreams, make smart tradeoff decisions as priorities shift, and translate ambiguous business needs into concrete technical architecture - Build reusable frameworks and engineering guidelines that raise the team's collective execution muscle Qualifications - 8+ years of software engineering experience with a track record of owning complex systems end-to-end - A software engineering foundation first — you think in systems, architecture, and engineering tradeoffs, not in models and experiments - Production experience building and shipping agentic workflows, multi-agent orchestration, HITL pipelines, and LLM-powered applications with measurable business outcomes — RAG, vector stores, semantic search, and multi-model LLM stacks in production, not just demos - Battle-tested context engineering practices — you reason clearly about the limits of AI and architect around them - Experience with distributed systems architecture applied to AI or data platforms — reliable, observable, and scalable systems built in service of a product - Daily proficiency with agentic coding tools (Claude Code, Cursor, or equivalent) — you use these to multiply your output, not pad it - A track record of operating in ambiguity — shipping fast, pivoting when wrong, and moving on without ego - Exceptional written and verbal English communication skills — you can lead a design discussion, push back on stakeholders, and document architecture clearly. Communication cannot be a bottleneck 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. This is the dream background. - Experience building human data labeling interfaces, annotation workflows, or data collection pipelines - Familiarity with preference data and reward models used in AI model training (RLHF, RLVR, or similar) - Proficiency with our stack: Python, TypeScript, AWS, GCP, Terraform, Temporal Cloud, containerization, LLM gateways, RAG frameworks, and data pipeline tooling - Ability to employ data structures and algorithms when forming AI/LLM solutions - Ability to reason about requirements with a bias for Essentialism