Sr Forward Deployed Engineer

Description

The company has led the subscription economy for over a decade and is now rebuilding its platform as the core of quote-to-cash, with an adaptable, AI-ready foundation for selling and monetizing products and services.

The platform supports subscriptions, usage-based pricing, AI-driven offers and outcome-based models, helping customers launch products, automate complex billing and secure steady, recurring revenue.

Forward Deployed Engineers are full-time members of Product & Engineering who work inside customer engineering teams to build the integrations, prototypes and agents that standard channels cannot reach. Internally, the role is called a Delta.

You work in a pod with a Forward Deployed Product Manager and, when scope calls for it, a second engineer.

The goal is to build the ideal end-state integration alongside a customer's software engineers: real, production-grade code taken from idea to production in weeks. You will surface what the platform cannot do yet, close the gap quickly and document what product should build next.

You will partner with Engineering, Product Management, Product Marketing, Sales, Services and Support so that what you learn in the field becomes a capability for the whole company.

The focus is agentic quote-to-cash: helping enterprises monetize AI-native and usage-based products, and bringing the company's new AI agents into their first customer deployments.

Responsibilities

• Build integrations, prototypes and agents with customer engineers in fixed-time engagements lasting weeks to months. Deliver production-quality code with a defined destination and a named maintainer.
• Write the specification with your Forward Deployed Product Manager, and agree scope and success measures with the customer before you build.
• Within the first few weeks, prototype on the customer's real data, then harden the work for production with security review, monitoring and evals for AI workflows.
• Design and ship production agents and multi-agent workflows, including tool and function calling, sub-agents, orchestration, and MCP servers and clients. Engineer context and retrieval for quality, latency and cost.
• Connect the platform to the customer's existing stack, including third-party metering and entitlement tools and ERP, CRM and finance systems. The platform remains the order-to-revenue system of record.
• Build AI monetization integrations: usage and AI-credit metering feeds, credit wallets and drawdown, and the billing and revenue flows that depend on them.
• Take the company's new AI agents into first customer deployments and extend the platform to new use cases and customer segments.
• With your Forward Deployed Product Manager and customer experts, build eval harnesses and golden datasets, set acceptance thresholds, and run evals in CI so every change is gated.
• Add guardrails and human-in-the-loop controls: least-privilege access, approval gates for write actions and segregation of duties.
• Instrument agents with tracing and logs of prompts, inputs, outputs and model versions so any action can be rebuilt for audit.
• Use AI coding agents such as Claude Code or Cursor as a daily habit, and review their output to production standard.
• Log product gaps as gap reports for the roadmap. You are the closest read on what customers need built.
• At exit, write the engagement Brief covering what was built, what broke, what can be reused and where it should live next. Turn proven work into reusable components.
• Hold the warranty window, roughly 30 to 60 days after go-live, then hand over to the named maintainer with runbooks and documentation. FDEs do not keep ongoing maintenance accounts.
• An engagement is finished only when the customer's team can run the solution without you. Training them is part of the build.
• Delegate to agents and bring in company engineers, partners and the customer's engineers so the work moves faster than one person could manage.
• Leave behind tools, components and agents that speed up the next engagement.
• Onboard new teammates during live engagements using a watch one, do one, teach one sequence.

Requirements

• Builder: you have shipped a product, integration, agent or tool that real people use. Be ready to walk through your decisions, trade-offs and failures.
• Act like an owner: weigh cost, speed and customer impact, make the call, and fix broken things even outside your scope.
• Take the hardest problem on an engagement, own the result and drive it through to the end.
• Stay polite and persistent when work stalls: follow up, find another route and get to yes without damaging relationships.
• Curious: you take systems apart to understand them, whether that is a customer's stack, a new model or an unfamiliar API.
• Prefer a working build to a deck about one.
• Have delivered results by bringing in people and agents beyond your own team.
• AI-first: you use AI coding agents every day. Be ready to show a real example of what you built, how you directed the agent and how you checked its output before release.
• 5+ years of professional software engineering, including customer-facing delivery or startup engineering.
• Strong integration and API engineer with production code against external or partner systems, not only internal services. Python or TypeScript (Java a plus), REST, webhooks, event-driven patterns and solid SQL.
• Have taken code to production yourself, including security review, observability and on-call readiness, with sound security judgment for systems that handle financial data.
• Hands-on experience building production LLM applications or agents (tool calling, retrieval, prompt engineering) and evaluating their output before customers see it.
• Quick, and wary of overbuilding: comfortable shipping in weeks to months, not quarters or years.
• Able to hold the room with a customer's senior engineer, explain trade-offs to finance users and architects, and communicate clearly under ambiguity.
• Document work without being asked: specs, gap reports and the exit Brief are part of the job.
• Low ego. This is a founding role where the description will change and you help shape its next version.
• Founder or early-team experience, ideally in AI monetization, agentic product development and deployment, or quote-to-cash: CPQ, contracts and orders, usage metering and rating, billing, payments, collections and AR, and revenue recognition.
• Experience with subscription billing, usage-based pricing, payments or monetization platforms.
• Exposure to agentic or usage-based AI pricing, AI agents, MCP servers or agent frameworks, or a past customer-facing or forward-deployed engineering role.
• ERP, CRM or finance integration experience (NetSuite, SAP, Salesforce, Workday or similar), and familiarity with SOX or IT general controls.
• The process includes a deep-dive walkthrough of something you built, including how you used AI tools, and a discussion of a time you pushed something through after it stalled. They assess judgment (what you delegated, what you checked, when you stopped trusting the output), curiosity and ownership more than speed.

Conditions

Full-time role in Product & Engineering.

Annual base pay range: $208,600 to $312,900 USD.

Final base salary depends on qualifications, job-related knowledge, prior related experience, unique skills, role location, internal equity and budget.

Base pay is one part of total rewards, which may also include bonus or variable pay, equity and benefits.

Applicants from every background are welcome.

Benefits

• Total rewards package focused on wellbeing, growth and flexibility. Offerings may differ by country, but typically include:
• Competitive pay, variable bonus and performance rewards, and retirement programs
• Medical, dental and vision insurance
• Flexible time off, paid holidays, wellness days and a company-wide year-end break
• Paid parental leave, fully paid for eligible employees where local policy allows
• Learning and development stipend
• Volunteering opportunities and charitable donation matching where available
• Mental wellbeing resources and support
• Benefits vary by location, and details are shared during the interview process.