About Rippling

Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system.

Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.85B+ from the world’s top investors — including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock — and was named one of America's best startup employers by Forbes.

We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses.

Why this role exists

Model quality is converging across providers. The differentiator that remains — and compounds — is the layer around the model: how it holds context, recovers from tool failure, knows what it's allowed to touch, and gets measurably better over time without a human rewriting its instructions every week. That layer is the harness. It is the difference between a demo and a system you can put in front of a customer's payroll data.

We're hiring the PM who owns that layer for Rippling's agents: our own harness, the memory systems that feed it, and the self-improvement loop that tunes it. This is not a PM role bolted onto an engineering team's backlog. You will make build-vs-buy calls on runtime architecture, define what "done" means for a memory system, and be the person who can sit in a design review with staff engineers and catch a bad abstraction before it ships.

What you'll own

Harness: Inform the architecture that turns a model into a worker, including how it plans, calls tools, interprets results, and decides to continue, retry, or escalate. You'll define the contract between orchestration and the harness, any required guardrails, and how to refine the harness to support the agents we’re building across Rippling.

Memory: Working memory inside a single run, episodic memory across sessions, understanding and defining the boundary between what belongs in context versus what belongs in a tool call. You'll help define what gets persisted, what gets compacted, and what gets forgotten on purpose.

Self-improvement: The feedback loop that makes the harness better without a human editing prompts by hand — eval-driven tuning, trajectory review, counterfactual benchmarking against prior versions. You own the definition of "improved": task completion rate, escalation-to-human rate, cost and latency per successfully completed task, and regression rate on the eval suite you help build.

Infrastructure: Sandboxing and execution environments, permission tiers and connector-level ACLs, credential handling so the model never sees a raw secret, and the observability stack that lets an engineer answer "why did this agent do that" six weeks after the fact.

New platform capabilities: As the core harness matures, you'll extend it into computer-use and autonomous web/research capabilities — agents that navigate interfaces without an API, and multi-step research tasks that synthesize across sources. 

Who you are

• A former software engineer-turned-PM with 5+ years of combined experience. You've shipped production systems yourself, not just specced them. 
• You've built or shipped agentic systems in production, not just prototyped them in a notebook. You can speak precisely about the difference between an agent framework (the blueprint) and a harness (the runtime that actually executes and recovers), and you don't use the terms interchangeably.
• You think in state machines and failure modes by default: what happens when the tool call times out, when the model hallucinates a tool that doesn't exist, when two agents claim the same lock. You've debugged distributed systems before you ever wrote a spec for one.
• You have a strong point of view on evals and can design a benchmark that predicts production behavior instead of rewarding overfit.
• You're fluent in the concepts this space actually uses day to day: context window management, tool orchestration, RAG vs. semantic vs. episodic memory, OAuth grant types and blast radius, sandbox isolation, RL/fine-tuning vs. non-parametric adaptation.
• You default to writing the design doc yourself when the team is moving too fast to wait for consensus, and you're just as comfortable being told your design is wrong by an engineer with more context.

What this role is not

It is not a backlog-grooming PM seat on an existing engineering-driven roadmap. It is also not a research-adjacent role for someone who wants to stay theoretical. You will be accountable for what ships and what it costs when it's wrong.

Additional Information

Rippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accomodations@rippling.com

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a defined radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location here.

A variety of factors are considered when determining someone’s compensation, including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.