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Senior Software Engineer, Agents

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Remote from
USA, Canada, EMEA+1 more, LATAM
Salary
USD 230k–230k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
19 Nov 2026
Listing views
22
Application actions
1
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AI Summary

The role, at a glance.

RevenueCat is hiring a senior software engineer to build production AI agents for subscription analytics, pricing experimentation, paywall creation, and controlled customer-facing actions. The role owns agent foundations including orchestration, tool execution, context management, evaluations, observability, permissions, and human-in-the-loop controls. This engineer will independently scope and ship user-facing capabilities, improve reliability and performance, and contribute to architecture across backend, frontend, and data systems. The position requires substantial production engineering experience, strong asynchronous communication, customer focus, and comfort operating in an ambiguous, high-ownership remote environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior, high-impact role involving reliable agentic systems that affect monetization workflows at significant scale. It requires deep backend judgment alongside ownership of ambiguous product, reliability, security, and evaluation challenges.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$230,000
US market range (per year)$200k–$280k
AI insightThe employer discloses a yearly base compensation amount of $230,000 USD. For a senior engineer building production AI-agent infrastructure in the US market, the estimated annual market range is $200,000–$280,000 USD; equity may be additional but is not quantified in the posting.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an AI agent you shipped to production that could take actions rather than only generate text.

I would explain the user problem, the tools and permission boundaries I designed, and how the agent converted model output into validated structured actions. I would also cover safeguards such as approval steps, idempotency, audit logs, and rollback behavior, then share adoption and reliability results.

How would you design an evaluation system for an agent that analyzes subscription-revenue changes and recommends actions?

I would define representative scenarios and expected outcomes across data retrieval, reasoning, recommendation quality, and action safety. I would combine automated regression tests, tool-call validation, offline labeled evaluations, production telemetry, and targeted human review, with clear thresholds that block unsafe releases.

What approaches do you use to make LLM-enabled workflows reliable and observable in production?

I instrument each stage with traces, structured logs, tool-call outcomes, latency, token usage, error rates, and user feedback signals. I also use typed schemas, deterministic validation, retries with limits, fallbacks, versioned prompts and tools, and dashboards that connect failures to specific model, context, or integration changes.

Tell us about a time you made a significant technical decision with incomplete requirements.

I would describe how I identified the irreversible decisions, gathered input from customers and stakeholders, proposed a small reversible initial design, and documented trade-offs. I would then explain how I shipped an incremental version, measured outcomes, and evolved the architecture based on evidence.

How would you balance speed of delivery with safety when an agent can modify a customer’s subscription configuration?

I would separate analysis from execution and require explicit user confirmation for consequential changes. The execution layer would enforce authorization, schema validation, policy checks, dry-run previews, idempotency, complete audit trails, and monitoring, allowing the team to ship useful functionality while containing operational risk.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

RevenueCat gives app businesses the infrastructure and tools to build, run, and improve their monetization. Since graduating from YC’s S18 batch, we’ve grown into the default monetization platform for mobile.

We’re in 50%+ of newly shipped subscription apps, we process $16B+ in annual purchase volume, and we help everyone from a solo developer to the OpenAI mobile team understand and grow their revenue.

We’re a remote-first team of 150+ people across 25+ countries, guided by values we actually practice: Customer Obsession, Always Be Shipping, Own It, and Balance.

This isn’t the right fit for everyone. The systems you’ll build here manage billions of dollars and touch hundreds of millions of end users, and we don’t take that lightly. You’ll be expected to work hard and hold a high bar for what you ship, alongside some of the sharpest, most driven people you’ve worked with. If that sounds energizing rather than exhausting, keep reading.

The Role

RevenueCat helps app developers manage subscriptions and make more money from them. Maybe you’re an indie developer who doesn’t know what “conversion” even means, you just want your app to make money so you can keep building it. Or maybe you’re a growth specialist at a company doing millions a month, and pricing experiments are already your whole job. Either way, you probably didn’t get into this to spend your days staring at a dashboard. So we built two AI agents to do that work for you, or make you even sharper at it if you’re already a pro.

Enter Rico. Rico watches over your subscription business and can help you make sense of it, and act on what it finds. Say your revenue dropped last week. You don’t need to go digging through charts to find out why. Just ask Rico. It pulls the data, explains what happened in plain language, and shows you how you compare to similar apps. Then it can fix things itself, like setting up a new plan or restoring a customer’s access, with you approving the change before it goes live. Run a pricing experiment and Rico will read the results for you and tell you which version is winning. There are so many things we want Rico to be able to do!

Then there’s our AI Paywall Editor, a specialized agent to build and edit paywalls, the screen that asks someone to subscribe. Most developers are not designers, and a paywall that actually gets people to tap “subscribe” is harder to build than it looks, so the AI Paywall Editor helps you with that. Use it in our online editor, from the command line, or hook your own AI tools into it over MCP.

You can see both agents in action in our most recent product launch recap.

This is a role for engineers who’ve built advanced agents that do actual work, not just a thin wrapper around an LLM. You’ll ship agentic features to thousands of developers, and you’ll shape the foundation everything else runs on: orchestration, tool execution, context management, evaluation and human-in-the-loop controls.

About You

  • You have 8+ years of experience shipping production systems.

  • You’ve built agents that other people actually use and the systems around them: tool use, structured output, context management, evaluation, orchestration.

  • You have strong backend fundamentals. You’ve built systems that need to be reliable, observable, and secure.

  • You hold a high bar for quality and instinctively know which details matter, but also are discerning enough to cut scope when needed while still delivering value.

  • You’re customer-obsessed: you imagine yourself as the developer, product manager, or marketer using the feature or experiencing the issue.

  • You’re a builder, not an implementer: scoping, prioritizing, and driving rollout in partnership with our Product team. This isn’t a role where you check off items from a detailed specification.

  • You can quickly familiarize yourself with code you didn’t write yourself and improve it.

  • You’re scrappy and biased toward shipping. You’d rather ship a good version today and iterate than wait to ship a perfect one next quarter.

  • You communicate clearly, both in real-time and asynchronously. We’re a fully remote team, and how you write is as important as how you code.

  • You’ve shipped meaningful user-facing features to production, monitored their usage and adoption, and improved them over time.

  • You write high-quality, well-tested code, debug issues methodically, and help raise the engineering bar through thoughtful feedback and mentorship.

  • You act like an founder:: you notice problems, propose improvements, and follow through fast.

  • You’re comfortable with ambiguity. This is an early stage product inside a growth stage company, and the architecture is still being figured out.

  • You’re self directed. You figure out what needs to be built, build it, and ship it.

  • You are curious and stay current on AI tools and practices.

  • You have a habit of sharing what you learn with others.

Ideally, you have:

  • Familiarity with subscription business models, app stores, or developer tools

  • Experience with OpenAI/Anthropic APIs specifically

  • Experience with observability frameworks (Langfuse, Langchain, etc.)

  • Built Slack integrations

  • Contributed to open source AI/ML tooling

  • Maintained systems at scale

In the first month, you’ll:

  • Meet frequently with your team and manager to get up to speed

  • Learn our tools, processes, and the subscription business concepts that shape our product

  • Get familiar with our debugging tools, infrastructure, and overall architecture

  • Ship your first project: a small feature that gets you into the codebase and shows you how the agents talk to RevenueCat’s APIs

  • Jump in early: fix bugs, ship small improvements, and weigh in on PRs

Within the first 3 months, you’ll:

  • Ship a medium sized project, ideally a new agentic feature that reaches tens of thousands of developers

  • Understand the main product areas your team owns and how they work end to end

  • Understand our current agent infrastructure, orchestration, the eval pipeline, the tool framework, and the trust and permissions layer, and have opinions on how to improve it

  • Scope and work on projects on your own, from idea to post launch improvements

  • Take part in code reviews, bug bashes, and technical discussions

Within the first 6 months, you’ll:

  • Become the go to engineer for certain focus areas

  • Have made the agents measurably more reliable, faster, and capable

  • Contribute to architecture discussions that affect more than just your team

  • Mentor other engineers joining the team

  • Talk directly with customers to understand how your work actually lands

  • Debug issues across frontend, backend, and data. Communicate clearly during incidents and drive fixes that prevent repeats

  • Learn the basics of incident response and join the on call rotation

Within the first 12 months, you’ll:

  • Have built infrastructure the rest of the team ships agents on top of

  • Know all the major components of our system and be able to debug complex issues

  • Write proposals and drive major architecture discussions

  • Spec and architect large projects, gather feedback, and drive design validation and rollout plans

  • Have made RevenueCat an AI native company in large part because you built the engine underneath it

  • Have your own initiatives for improving our product and systems, grounded in real customer priorities

What we offer:

  • Competitive equity in a fast-growing, Series C startup backed by top-tier investors, including Y Combinator

  • 10-year window to exercise vested equity options

  • A fully remote environment designed around autonomy and flexibility

  • 4-5 weeks of flexible time off annually

  • Paid desk at a co-working space

  • Workspace budget and continuous learning stipend

Interviewing at RevenueCat

Our interview process is rigorous on purpose. We want to be sure that everyone we bring on is genuinely excited about this work, aligned with how we operate, and ready to meet our high bar for performance. Curious what that actually looks like? Read more in our blog post on how we hire at RevenueCat, including tips to help you succeed.

More on how we work:

Apply now >

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