Pareto Ai
Applied AI Engineer
3mo ago
225000 –275000 USD / yearUSASeniorRemotepythonrl environmentworkflow automationsystem design
Design and build AI training pipelines and human-AI workflows for scalable reinforcement learning environments.
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
Requirements
- 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
Other
- 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 .
- At Pareto, we build the platform that turns that judgment into the data , evals , and RL environments frontier models 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.