Senior Software Engineer - AI Initiative

About the AI Platform Team

The AI Platform team is one of Rippling's highest-priority engineering investments and sits at the center of the company's broader AI strategy. The team is building the foundational infrastructure that powers AI capabilities across Rippling's entire product ecosystem, including HR, Payroll, Benefits, Recruiting, IT, Finance, Compliance, and Workforce Management. Rather than building standalone chatbots or simple LLM integrations, the team focuses on creating deeply integrated AI systems that understand Rippling's rich business context, permissions model, workflows, and enterprise data.

The team is developing core platform capabilities such as AI agents, workflow automation engines, evaluation frameworks, feedback loops, data pipelines, and self-healing systems that can identify issues, surface insights, and help automate complex enterprise workflows. These systems operate with real business context, approvals, permissions, and auditability requirements, enabling customers to safely delegate operational tasks while maintaining control and governance.

One of the unique aspects of Rippling AI is that it is built directly on top of Rippling's unified data model and permission system. The platform can reason across HR, IT, Payroll, and Finance data while respecting access controls and providing verifiable outputs. The team is solving challenging problems around agent orchestration, workflow execution, reliability, permissions, evaluations, observability, data quality, and scalable AI infrastructure.

This is an ideal environment for engineers who enjoy building large-scale backend and platform systems, working through ambiguity, and owning products end-to-end. Engineers on the team are highly hands-on, spend the majority of their time coding, and have the opportunity to shape how AI becomes a core part of enterprise software. The role offers significant ownership, visibility, and a strong path toward Staff-level engineering scope over time.

What you will do:

• Build foundational AI platform systems that support Rippling’s broader AI strategy across HR, IT, Finance, payroll, benefits, recruiting, compliance, and internal workflows.
• Work as a deeply hands-on Senior Software Engineer, with around 60% of time expected to be spent coding, debugging, reviewing code, and owning implementation.
• Contribute to the design and build-out of AI platform capabilities such as background agents, automated workflows, evaluation systems, data pipelines, and model quality feedback loops.
• Help build systems that allow AI agents to automate repetitive enterprise workflows with the right permissions, controls, approvals, and auditability.
• Work on platform systems that support self-healing and self-improving workflows, where AI can detect negative product signals and help identify root causes.
• Partner with Staff Engineers, Product, Infra, Platform, and Engineering stakeholders to convert ambiguous product/platform problems into scalable technical solutions.
• Own well-defined to moderately ambiguous components end-to-end — from design and implementation to testing, launch, monitoring, and iteration.
• Participate actively in design discussions, code reviews, debugging, production support, and operational improvements.
• Build reliable, scalable, and maintainable backend/platform systems that can support Rippling’s product ecosystem.

What’s exciting about this role:

• This is not a generic chatbot or lightweight LLM wrapper role. The team is building core AI platform infrastructure that can make Rippling’s product surface more autonomous, context-aware, and self-improving.
• Engineers will get exposure to AI agents, automated enterprise workflows, self-healing systems, and custom intelligence layers built around Rippling-specific product context.
• The role offers strong learning, ownership, and growth opportunities for engineers who want to work at the intersection of backend/platform engineering and AI infrastructure.
• This is a strong opportunity for a high-potential Senior Engineer to grow toward Staff-level scope over time.
• Rippling AI is a high-priority company initiative, and the team needs strong Senior Engineers who can add meaningful hands-on execution capacity.
• The AI Platform team is lean and supports a broad set of internal engineering and product stakeholders, creating a need for engineers who can ramp quickly and contribute with high ownership.
• The team needs builders who are strong in backend/platform engineering and are comfortable working on scalable production systems.
• The systems being built involve complex areas such as workflows, permissions, approvals, data quality, reliability, evaluations, and automation.
• Generic LLMs do not fully understand Rippling’s proprietary workflows, policy logic, query language, company data, and permissions, so the team needs engineers who can help build AI infrastructure around Rippling’s real product context.
• The team moves fast, and candidates need to be comfortable with ambiguity, changing priorities, and rapid iteration.
• Coding is a core expectation for this role; this cannot be filled by someone who is not actively hands-on.
• Start by owning meaningful components within AI Platform initiatives such as background agents, workflow automation, evaluations, data pipelines, or self-healing systems.
• Grow from owning individual components to owning larger services, workflows, or platform modules end-to-end. Build depth in Rippling’s product context, AI platform systems, infrastructure, and internal engineering patterns.
• Partner closely with Staff Engineers and senior technical leaders to learn how larger architecture and platform decisions are made. Increase influence through high-quality coding, strong debugging, reliable execution, thoughtful design input, and ownership after launch.
• Over time, grow toward Staff-level expectations by taking on more ambiguous problems, mentoring other engineers, and influencing broader technical direction.
• Become a trusted engineer who can independently deliver scalable, reliable, production-grade systems with limited hand-holding. Ability to ramp quickly into Rippling’s AI platform, codebase, product context, and internal systems.
• Strong hands-on coding contribution, with approximately 60% of time spent coding and implementation-focused work.

What you need to have:

• 5–8 years of overall software engineering experience, preferably in backend, platform, infrastructure, distributed systems, or product engineering.
• Strong hands-on coding ability; 60% coding is non-negotiable. Must be comfortable writing production-quality code, reviewing code, debugging issues, and owning implementation details.
• Strong programming knowledge in one or more backend/general-purpose languages such as Python, Java, Go/Golang, C++, Scala, Kotlin, or C#.
• Language should not be the only filter — coding fundamentals, system design depth, distributed systems exposure, and ability to build production-scale systems are more important.
• Experience building and operating scalable backend/platform systems in production.
• Good understanding of system design, API design, databases, data modeling, concurrency, observability, and production troubleshooting. Ability to independently own well-defined to moderately ambiguous technical problems from design to execution, launch, and iteration.
• Comfortable working in a fast-paced product engineering environment with changing priorities and high ownership.
• Strong collaboration skills with Product, Infra, Platform, and Engineering stakeholders. Good product judgment and ability to understand how engineering decisions impact customers and business outcomes.
• Ability to contribute to design reviews, code reviews, technical discussions, and team quality.
• For AI Platform specifically, practical curiosity or exposure to AI agents, LLM infrastructure, agentic workflows, ML/data infrastructure, inference pipelines, evaluations, automation systems, or adjacent AI systems is a strong plus.

What would be nice to have:

• Prior experience in a high-growth or hyper-growth startup / product-led technology company.
• Recent hands-on exposure to AI agents, LLM tooling, AI infrastructure, automation platforms, evaluation systems, or data/ML pipelines.
• Strong knowledge of Python for AI/ML infrastructure, automation, agents, scripting, evaluations, or data pipelines.
• Strong SQL knowledge for querying, data-heavy systems, analytics, workflow systems, reporting, and enterprise intelligence use cases.
• Experience with Go, Java, C++, or Scala in backend, platform, infrastructure, or distributed systems environments.
• Exposure to TypeScript / JavaScript if the role involves full-stack, internal tooling, dashboards, or product surface ownership.
• Experience with workflow orchestration, background automation, reliability platforms, internal tools, or developer productivity platforms.
• Exposure to permissions, approvals, auditability, compliance, or security-sensitive workflows.
• Prior exposure to fintech, HR tech, payroll, benefits, compliance, IT, enterprise SaaS, or workflow-heavy platforms.
• Clear evidence of learning quickly, taking ownership, and operating well in ambiguous environments.

Technical Skills:

• 5–8 years of strong software engineering experience Preferably in backend, platform, infrastructure, distributed systems, or product engineering.
• 60% hands-on coding is non-negotiable Candidate must be actively writing, reviewing, debugging, and owning production code. This is not a coordination-only or design-only role.
• Strong coding ability in at least one backend/general-purpose language
• Preferred languages: Python, Java, Go/Golang, C++, Scala, Kotlin, or C#. Scalable backend/platform systems exposure
• Should have worked on production systems involving APIs, databases, reliability, observability, debugging, and scale.
• Ownership mindset Should be able to independently own features/components end-to-end, work through moderate ambiguity, and drive execution without constant hand-holding.
• Strong learning agility and AI/platform curiosity For AI Platform, candidate should show practical curiosity or exposure to AI agents, LLM infrastructure, automation systems, ML/data infra, inference, evaluations, or adjacent AI systems.