Backend Software Engineer, Spendesk

• This role requires both strong backend engineering and genuine interest in AI-powered product design. You won’t be expected to build the models yourself, but you will be responsible for making them usable, scalable, secure, and effective in production
• Significant experience on backend software engineering experience in production environments
• A strong track record of designing and shipping reliable backend services with measurable user or business impact
• Experience contributing to complex product initiatives in fast-paced, cross-functional teams
• Exposure to ML-enabled or AI-enabled product features is a strong plus
• Strong backend engineering skills with TypeScript / Node.js or adjacent technologies
• Experience designing APIs and service layers for complex product workflows
• Good understanding of distributed systems, async processing, and operational reliability
• Practical experience, or strong interest, in integrating predictive models, LLM APIs, or other AI capabilities into product backends
• Familiarity with technologies such as Kafka, SQS, Step Functions, PostgreSQL, and modern observability practices
• Highly autonomous and comfortable owning backend systems from design to production
• Product-minded, customer-focused, and motivated by building features that create visible value for end users
• Comfortable working closely with ML Engineers and translating their outputs into durable product capabilities
• Pragmatic and impact-driven, able to move from experimentation to production without losing engineering rigor
• Fluent in written and spoken English, our business language
• Not ticking every box? We’d still love to hear from you. At Spendesk, we value skills, potential and diverse experiences. If this role excites you and you believe you could contribute, we encourage you to apply

Desirable

• Spendesk is looking for a Backend Software Engineer (IC3) to join our AI & Data Products squad and help build the next generation of product features powered by AI, ML, and intelligent automation
• This is a hands-on backend role focused on turning predictive models, LLM capabilities, and business intelligence into real product experiences
• You will work on backend services, APIs and MCPs that bring automation, prediction, and assisted decision-making into Spendesk’s user journeys, helping reduce manual work and make our product more proactive and intelligent
• You will collaborate closely with the 3 ML Engineers in the squad, as well as Product Managers, Designers, and applicative squads across Spendesk
• Together, you will build production-grade services that expose ML-driven and LLM-driven capabilities in ways that are reliable, observable, and valuable for end users
• As a Backend Software Engineer (IC3) in the AI & Data Products squad, you will design, build, and operate backend services that power AI-native and ML-native product features
• Your mission is to help Spendesk move from isolated intelligence components to real, user-facing product capabilities
• In practice, this means partnering with ML Engineers to productionize predictive logic, expose it through clean APIs and services, and integrate it into workflows that automate tasks, simplify decision-making, or anticipate user needs
• You may work on features such as:
• Automated categorization and enrichment of spend-related workflows,
• Predictive assistance in finance or accounting journeys,
• Intelligent recommendations based on historical behavior or contextual signals,
• LLM-powered experiences that simplify user actions and reduce friction,
• Backend services that make AI capabilities reusable across multiple product flows
• You do not need to be an ML engineer, but you must be comfortable integrating predictive or generative capabilities into backend systems and user-facing product workflows
• Design, build, and operate backend services and APIs that power AI-driven, ML-driven, or automation-heavy product capabilities
• Translate predictive logic and AI outputs into reliable backend behaviors that can be consumed by user-facing product flows
• Build the service layer that allows intelligent features to be integrated into real workflows with strong standards on latency, reliability, and security
• Ensure features are designed for production, not just experimentation, with clear ownership of deployment, monitoring, and maintainability
• Partner closely with the squad’s ML Engineers to productionize predictive models and LLM-driven capabilities
• Integrate model-serving APIs or LLM calls into robust backend services (your squad, or the applicative squad’s services) with proper retries, fallbacks, and observability
• Help define evaluation and monitoring patterns that make intelligent product behaviors measurable over time
• Contribute to the engineering patterns that allow ML and AI capabilities to be reused across multiple product features
• Build backend capabilities that help automate repetitive tasks, anticipate user needs, or simplify complex workflows
• Work on product experiences where AI or ML can reduce manual effort, improve decision quality, or shorten time to value for users
• Partner with Product and Design to turn ambiguous ideas into concrete backend implementations with measurable impact
• Bring pragmatism to delivery, balancing experimentation speed with long-term maintainability and trust
• Instrument services with logs, tracing, and metrics to support production visibility and continuous improvement
• Define and uphold standards around latency, resilience, failure handling, and cost efficiency for AI-powered services
• Build with responsible data handling, security, and privacy by default, especially when features interact with sensitive financial workflows
• Embrace a “you build it, you run it” mindset, owning the health and quality of what you ship
• Work hand-in-hand with ML Engineers, Product Managers, and Designers to deliver AI-powered product capabilities end-to-end
• Collaborate with applicative squads (or join them for a quarter) to integrate AI and ML services into existing user journeys and backend systems
• Help define the technical interfaces and integration patterns that make intelligent services easier to adopt across the product
• Share best practices in backend reliability, production readiness, and AI feature delivery across the engineering organization
• What success looks like in your first 90 days
• You’ve shipped or materially advanced a production-grade backend service powering an AI-driven or ML-driven product capability
• You’ve partnered effectively with one or more of the squad’s ML Engineers to turn predictive or generative logic into a reliable user-facing backend flow
• You’ve improved the production readiness of an intelligent feature, for example through better observability, service integration, fallback handling, or evaluation metrics
• You’ve contributed to a reusable backend pattern that makes future AI-powered product features easier to build across Spendesk

• HR screening call
• Discussion with the Hiring Manager
• Technical interview, live coding and/or system design depending on profile
• Final interview with leadership