Senior Backend Engineer

Jeeves is the stablecoin-native banking platform for global enterprises. Thousands of companies across 35 countries run their corporate cards, payments, treasury and spend on one platform, with more than $5B in annualized volume, payouts to 190 countries, and one of the fastest-growing stablecoin payment networks in the world. BMW, H&M, Lululemon, Burger King, Kavak and XP are among them, and more than 80% of customers use multiple Jeeves products. We chose hard problems on purpose. We hire for one thing: relentlessness. A strong motor that keeps going when the problem has no playbook. You'll know in your first week if Jeeves is right for you. For the right people, it's the steepest learning curve of your career. Jeeves has raised more than $400 million in equity funding from Andreessen Horowitz, Y Combinator, CRV, Coinbase Ventures, GIC, AllianceBernstein, CoinFund and others. If you want to rebuild how money moves across borders, come build it with us. Jeeves is looking for a Senior Backend Engineer to join our engineering team. You will design and build the backend systems that power Jeeves's financial platform — working across payments, cards, spend management, and compliance infrastructure that serves businesses across the Americas and beyond. This is a backend engineering role at its core, we're looking for a strong backend engineer who knows how to work effectively with AI tools, understands where AI can accelerate development and product capabilities, and is comfortable integrating AI-powered features into production backend systems. What You'll Do: Backend Engineering - Design, build, and maintain scalable, reliable backend services that process financial transactions and serve Jeeves customers across 20+ countries. - Write clean, testable, production-quality code in Go, Python, or Node. js/TypeScript; participate actively in design and code reviews.

- Build and consume RESTful and GraphQL APIs; design inter-service communication using gRPC, message queues, and event-driven architectures. - Design and optimize relational and non-relational database schemas (PostgreSQL, MongoDB, Redis) for correctness, performance, and scale. - Own backend features end-to-end — from scoping and technical design through deployment, monitoring, and iteration. - Implement security best practices: authentication, authorization, input validation, and data protection across distributed services. AI-Assisted Feature Development - Integrate LLM API calls (e. g. , OpenAI, Anthropic) into backend services as product features — such as spend categorization, document parsing, or natural language workflows — ensuring those integrations are reliable, observable, and cost-efficient. - Build backend pipelines that consume AI-generated outputs safely: validate structured outputs, handle fallback scenarios, and design graceful degradation when AI services are unavailable or return low-confidence results. - Collaborate with AI and data science teams to integrate model outputs into backend APIs — bridging experimental AI work and production systems. - Use AI coding tools (GitHub Copilot, Claude, Cursor, etc. ) fluently as part of your everyday development workflow. Reliability & Operations - Instrument services with structured logging, distributed tracing, and metrics for full operational visibility. - Participate in on-call rotation; respond to production incidents and contribute to post-incident reviews. - Contribute to CI/CD pipeline improvements, testing infrastructure, and deployment practices. Cross-Regional Collaboration - Work closely with engineering, product, compliance, and data teams across multiple time zones and regions — communicating in both English and Spanish or Portuguese as the situation requires. - Contribute to a globally distributed engineering culture through thorough documentation, async design reviews, and thoughtful pull request feedback.