Build and scale AI platforms for Rippling's Data Cloud, focusing on schema retrieval, query planning, LLM training pipelines, RL environments, and agent harnesses for intelligent workforce workflows. Requires 8+ years experience with production LLMs, distributed systems, and cloud infrastructure.
189k – 315k/yr
Hybrid8+ YOEML Engineering
About the role
What you will do
Develop a state of the art schema retrieval system that operates over Rippling native and customer defined schema.
Implement and scale Data Cloud’s AI training pipelines for data retrieval, from designing data generation to managing training clusters.
Design and build RL training environments for structured and unstructured data retrieval.
Develop agent harnesses enabling product teams to safely build AI features on Data Cloud.
Optimize model serving and inference for scale from GPU level performance to agentic user experiences.
Partner with AI Platform, Infrastructure, Security, and Product Engineering to drive architecture, standards, and rollout plans.
Lead technical direction, mentor engineers, and drive execution on multi-team, ambiguous initiatives.
What you will need
8+ years of software engineering experience, including significant ownership of distributed systems in production.
Experience post training and deploying LLMs in production environments.
Experience optimizing model inference at scale, particularly with LLMs and embedding models.
Strong backend engineering skills in one or more languages such as Python, Go, or Java.
Experience with cloud-native infrastructure (Kubernetes, container orchestration, observability, reliability engineering).
Ability to drive cross-functional technical strategy and execute through influence across teams.
Build foundational AI agent infrastructure at Rippling, owning agent creation, invocation, skill packaging, plugin connectivity, and automations. Lead platform and distributed systems work with 8+ years experience, technical leadership, and cross-functional impact.
189k – 315k/yrHybrid8+ YOEML Engineering
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