About the role
About Finom Finom is a European tech startup headquartered in Amsterdam, and we’re on a journey towards revolutionizing the financial landscape for entrepreneurs worldwide. Our mission is to develop an all-in-one financial B2B solution that integrates banking functions, accounting, financial management, and invoicing into a seamless, mobile-first platform. We recently closed a €115 million Series C equity round (around $133 million), bringing our total funding to approximately $346 million. This significant investment follows a $105 million growth funding round from General Catalyst, a long-term backer since 2021 known for supporting companies like Airbnb, HubSpot, KAYAK, and Stripe. Finom's platform goes beyond traditional banking, offering invoicing and a growing suite of features, including AI-enabled accounting, aiming to simplify financial management for entrepreneurs. We're actively expanding our reach across key EU markets like Germany, France, the Netherlands, Italy, and Spain. At Finom, we’re not just redefining the entrepreneurial experience — we’re empowering our employees to make a real difference. Your work matters, and your impact extends far beyond product metrics. We nurture innovation and an inspiring work environment where bold ideas thrive, prioritizing thorough research, swift implementation of solutions, and ensuring that every effort we make
benefits our users, employees, partners, and our business as a whole. Maintaining our start-up spirit, we prioritize thorough research, swift implementation of solutions, and ensuring that every effort we make
benefits our users, employees, partners, and, of course, our business. In this highly cross-functional role, you will be the gatekeeper of AI safety, performance, and deterministic behavior across non-deterministic multi-agent systems. You will not be filling out generic manual testing spreadsheets or operating in a vacuum \n What You Will Be Doing Architect Automated Evaluation Frameworks: Design, implement, and maintain scalable evaluation pipelines (Evals) for LLMs and agent graphs using modern tooling like LangSmith, DeepEval, Ragas, or Opik. Curate Ground-Truth Benchmarks: Collaborate with domain experts to build, version, and sanitize robust gold-standard datasets, synthetic evaluation profiles, and edge-case testing matrices reflecting real-world business scenarios. Own Non-Deterministic Quality Tracking: Define, monitor, and enforce quality KPIs across multi-agent workflows—specifically focusing on tool-calling accuracy, intent-recognition safety, structured output formatting, and context-retrieval (RAG) precision. Mitigate and Quantify Systemic Risk: Lead rigorous failure and hallucination analyses on production outputs. Implement structured LLM-as-Judge patterns, validation metrics, and guardrail heuristics while actively ensuring the judge profiles remain free of baseline evaluation bias. Enforce CI/CD Evaluation Gates: Partner directly with MLOps and Backend Engineering teams to integrate automated testing gates into our deployment pipelines, proactively preventing regressions or behavioral drifts from reaching production runtime environments. Drive Optimization for Latency & Cost: Regularly analyze the efficiency of prompt templates, few-shot structures, and model selections (e.g., GPT, Claude, LLaMA) to ensure a highly calibrated balance between execution throughput, sub-second latency, and platform compute costs.
