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/yr
Hybrid8+ YOEML Engineering
About the role
What you will do
Own major AI agent infrastructure initiatives across backend, platform, and AI-enabled systems
Lead design and execution for systems supporting agent creation, invocation, triggering, skill packaging, plugin connectivity, and automation orchestration
Build the foundational tools and primitives that enable both internal Rippling teams and external customers to create and deploy their own AI agents
Act as a technical lead for cross-functional work involving Product, AI Infra, Platform, and Engineering teams across the AI Cloud org
Break down ambiguous technical problems into clear architecture and execution plans
Write code, review critical code paths, debug production issues, and make implementation-level decisions daily
Build platform systems that are reusable across multiple Rippling product teams
Raise the engineering bar through design reviews, code reviews, mentoring, and technical guidance
Balance speed, quality, reliability, and long-term platform maintainability in a fast-moving environment
What you will need
8+ years of software engineering experience with a proven track record of technical leadership and org-wide impact
Deep expertise in platform and distributed systems engineering — you've built reliable, observable, large-scale systems
Strong hands-on coding skills, you are an active contributor in the codebase alongside your leadership responsibilities
Experience leading complex, cross-functional technical initiatives and driving alignment across multiple teams
Strong product instincts, you translate ambiguous problems into durable platform capabilities and partner effectively with product teams
Clear, precise communication to diverse audiences, both written and verbal
Comfort with ambiguity and high velocity, the roadmap evolves as the technology evolves
Strong curiosity around AI agents, LLM infrastructure, agentic workflows, automation systems, or plugin/tool-use architectures
Recent hands-on experience building AI agents is a plus
Skills
AI Agentsllm infrastructureDistributed SystemsPlatform Engineeringautomation systemsplugin connectivityAgentic WorkflowsPythonJavaTypeScript
Leads development of agentic AI systems for public sector, including guardrails, data processing, and fleet orchestration for federal datasets. Mentors engineers, defines technical strategy, and communicates with stakeholders to ensure reliable, secure solutions.
189k – 362k/yr
On-siteML Engineering
Staff Machine Learning Engineer, Computer Vision
PinterestSan Francisco, CA
Staff Machine Learning Engineer developing state-of-the-art visual encoders and multimodal models at Pinterest Labs. Prototype visual reasoning tools, train billion-scale models on rich visual-text data, ship to production for recommender systems and VLMs, publish research, and mentor juniors. Requires strong CV/ML background, publications, and PhD or equivalent.
189k – 390k/yr
Hybrid7+ YOEML Engineering
Staff Machine Learning Engineer
PinterestSan Francisco, CA +1
As a Staff Machine Learning Engineer, you will lead the ML strategy and execution for the Advertiser and Seller Experience team, building recommendation systems and context foundations. This role requires deep expertise in recommendation systems and modern agentic AI to shape advertiser and seller workflows.
189k – 390k/yr
Hybrid7+ YOEML Engineering
Staff Engineer, AI Platform & Architecture
Shield AIUnited States
Staff Engineer responsible for designing enterprise AI platform architecture, reusable components, responsible AI controls, observability, and governance patterns. Requires deep expertise in generative AI, RAG, agentic workflows, and influencing cross-functional teams without direct authority.
190k – 290k/yr
Remote7+ YOEML Engineering
Staff Software Engineer
DatabricksSan Francisco, CA
Build and optimize LLM inference infrastructure at enterprise scale for partner and self-hosted frontier models. Requires 8+ years backend/infrastructure engineering experience with distributed systems, real-time serving, and ML/GPU orchestration.