Applied AI Engineer

ABOUT PARETO Humanity is in a virtuous cycle: human insight improves AI, and better AI expands what people can do. Sustaining it depends on the one input that can't be automated: expert human judgment https://pareto.ai/blog/debating-persuasive-llms-truthful-answers. At Pareto, we build the platform that turns that judgment into the data https://pareto.ai/blog/community-driven-knowledge-resource-ai, evals https://pareto.ai/blog/introducing-attunebench, and RL environments frontier models https://pareto.ai/blog/llm-metacognition-shared-and-shallow learn from. We work with leading frontier labs like Anthropic and GDM, and we give skilled people everywhere a way to shape the future of AI and share in what it creates. This RL environment and human-data infrastructure is already in production. Our job now is to scale it. 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