• The Revenue Engine Team is making the foundational platform decisions that will shape how Headway moves money — between patients, providers, and payers — at the scale our growing network demands. As one of two staff engineers in the group, you'll partner with engineering leadership to set technical direction across our Payment Platform and Clearinghouse Platform teams. • This is platform work in the truest sense. Every payment that's charged or paid out, every claim that's filed and reconciled, every dollar that flows through Headway depends on the systems your teams own. As Headway's network grows — more patients, more providers, more payers, more transaction volume — the reliability, throughput, and correctness of these systems become the difference between a business that scales and one that doesn't. • You'll lead the design of fault-tolerant, observable, and deeply reconciled integrations with the third-party services we depend on. You'll build the abstractions and standards that let dozens of adjacent engineering teams — Billing Infrastructure, Patient Billing, Provider Payouts, and others — ship faster and more safely on top of our platform. • And you'll do it as a hands-on technical leader: writing code where leverage is highest, mentoring senior engineers across both pods, and shaping the operational excellence that this domain demands. • This role is for engineers who get energized by distributed systems problems with real consequences — concurrency, idempotency, partial failures, all with a substantial potential blast radius — and who want to apply that craft to a mission that actually matters: building a mental healthcare system that everyone can access.

• Unterstütze dein Team in der ständigen Weiterentwicklung und Neu-Implementierung im Bereich Konfiguration für das Online-Ticketing für Veranstaltungen • Konzipiere und entwickle neue Features: von der Analyse der Anforderungen bis zur Umsetzung der einzelnen Entwicklungsschritte • Wäge lösungsorientiert, pragmatisch und wirtschaftlich zwischen visionären Ideen und optimaler Software Architektur ab • Übernimm die Prozessüberwachung (Monitoring) und berücksichtige dabei besonders die Skalierbarkeit • Ein wenig Operation (DevOps, Terraform), in Zusammenarbeit mit unseren beiden Administratoren

Role Description We are looking for a Senior Ruby on Rails Engineer to help us build reliable, scalable products and apply AI/LLM technologies where they create real value. This is a hands-on engineering role for someone who can own complex features end to end, make strong technical decisions, and work across backend architecture, production systems, testing, and AI-assisted product development. You should be excited about AI, but pragmatic enough to know when a simple, deterministic solution is better than an LLM call. - Owning features from technical design to production - Building and maintaining Ruby on Rails systems used at scale - Designing AI/LLM-assisted features and workflows where they improve product quality or user experience - Working with LLM APIs, prompts, retrieval/context strategies, and AI-assisted development tools - Making trade-offs around performance, reliability, cost, latency, security, and maintainability - Writing and maintaining automated tests, including unit, integration, and E2E tests - Debugging production issues and improving system reliability Qualifications - Strong professional experience with Ruby on Rails, ideally at senior level - Experience building and running production systems used by real customers - Strong backend skills, including databases, API design, performance, background jobs, and security - Ability to break down ambiguous problems and propose practical technical solutions - Hands-on interest or experience with AI/LLM technologies - Good judgment about when to use AI and when simpler engineering approaches are better - Strong testing habits and care for long-term code quality - A responsible, curious, flexible mindset: willing to debug, deploy, investigate, learn, and own outcomes Requirements - Nice to have: - Experience with React - CI/CD, background jobs, or AWS experience - Experience building AI-powered product features in production - Familiarity with embeddings, vector search, RAG, evals, or LLM observability - Experience with text-processing, localization, translation, or multilingual products Benefits - Stable & self-funded: Founded in 2007, profitable, and independent. - Industry leaders: Creators of WPML, the #1 multilingual plugin for WordPress, used on 1.5M+ sites - Fully remote, async-friendly: Work from anywhere within EMEA hours - Engineering-driven culture: Ownership, autonomy, testing, and thoughtful problem-solving - Fair compensation and great benefits: Designed to support your well-being both inside and outside of work.

• Build and ship multi-agent orchestrator systems using Claude Code / agent frameworks • Design agent roles, workflows, and memory structures for real business operations • Implement coordination logic between agents (task delegation, state passing, retries) • Ensure systems are production-ready (not demos or notebooks) • Continuously refine agent behavior based on real-world execution outcomes • Convert business workflows into automated AI-driven pipelines • Build standalone agent SDKs and reusable automation modules • Design systems that execute multi-step business processes without human intervention • Ensure workflows are reliable, observable, and maintainable in production environments • Optimize for failure recovery, not just successful execution paths • Design and implement PostgreSQL schemas for agent systems and workflows • Build Supabase-backed applications powering AI agents and automations • Ensure data integrity across multi-agent operations • Structure databases for real-time agent execution and state tracking • Debug and optimize backend performance when workflows break • Build and maintain production API integrations across business tools • Connect AI agents to external systems (CRMs, property tools, communication tools) • Handle authentication, rate limits, retries, and failure states • Ensure integrations are stable and production-safe, not experimental scripts • Debug end-to-end system flows when integrations fail • Take systems from architecture → build → deployment → production use • Package AI systems into usable tools or SDK-like deliverables • Ensure systems are deployable, maintainable, and configurable • Write clean, production-grade code (not prototypes or notebooks) • Maintain and improve systems after deployment based on real usage • Identify missing requirements without being told • Break down ambiguous system requests into buildable components • Choose implementation paths independently • Debug production issues without escalation dependency • Maintain momentum without waiting for structured assignments