Senior/Staff Backend Engineer - Golang

Qonto

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ 🌏 Location: France, Germany, Spain, or Italy — full remote-friendly. Join us as a Senior Backend Engineer to build the AI-powered tooling that enables Qonto's financial crime compliance teams to make decisions faster and with greater confidence, directly supporting our path to a full banking licence. ➡️ As a Backend Engineer at Qonto, you will Build AI-integrated backend systems: Design and ship production-grade services that connect AI models (Claude, OpenAI, or open-source equivalents) to our compliance workflows, handling prompting, output parsing, and model behaviour in a real regulated-industry context Own service architecture end-to-end: Co-own design, reliability, and scalability from day one, no PM layer between you and the technical decisions that shape the product Automate AML/AFC compliance operations: Translate compliance agent workflows into software, from EDD automation to customer onboarding acceleration, with direct feedback from the ops teams you're helping Drive continuous improvement: From production monitoring to post-mortems, you track impact, flag regressions, and improve what's in your scope with your peers and manager Shape the technical direction: In a team of 5, every engineer carries architectural weight. You'll lead design discussions, anticipate risks, and contribute to how the platform evolves ➡️ What you can expect Short time-boxes and lean process: daily goals, light Kanban, ceremonies, you spend most of your time building, not in planning meetings AI is the product, not a side tool: this team's output is AI-powered compliance tooling. You'll use Claude or OpenAI to solve real problems, and you'll be expected to handle model behaviour reliably in production, not just use AI to go faster Direct roadmap ownership: no dedicated PM means you and the team define priorities, your judgment shapes what gets built next Close feedback loop: AML/AFC ops teams proactively raise needs and respond quickly to what you ship. Lead time reduction, prediction confidence, and agent throughput are the metrics that tell you it's working ➡️ Your future team You'll join a team of 5 engineers within Qonto's Financial Crime Compliance (FCC) domain, alongside Ioannis (Tech Lead) and a mix of Senior/Staff Backend Engineers and Machine Learning Engineers. You'll be part of a 190-strong backend engineering community within a 500+ Product Engineering department. One important clarification: this team does not build fraud detection engines or KYC/KYB rule engines. Those exist elsewhere at Qonto. This team builds the automation layer on top, turning model outputs into decisions that help compliance agents respond faster. It's a backend role at the intersection of AI integration and regulated-industry operations. ➡️ About you AI integration in production: You've shipped systems where AI (Claude, OpenAI, or open-source) is a core functional component, not a coding assistant, but the product itself. Strong backend foundation: Solid experience with large-scale web application architecture and real production constraints + Ideally experience in Python. Sound architectural judgment: You make confident system design decisions independently and can articulate the trade-offs. Product instinct without a PM: You're comfortable owning the full cycle: scoping, prioritizing, shipping, success metrics 

Qonto

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ 🌏 Location: You can choose to work in Qonto as long as you're living in (or willing to relocate to) either Germany, France, Italy, Serbia, or Spain. Mission: Join us as a Backend Engineer to build the financial infrastructure that 600,000+ European SMEs depend on every day, from highly scalable APIs to robust banking services that handle real money, in real time, with zero room for mistakes.   ➡️ As a Backend Engineer at Qonto, you will Design, build, deploy, and maintain services handling real financial transactions, owning reliability in production, not just at merge time Co-own service architecture, resilience, and scalability with respect to Domain-Driven Design principles Grow in technical leadership: lead design discussions, anticipate risks, and mentor less experienced engineers Drive continuous improvement, from fixing flaky CI to running post-mortems and propagating learnings across the team Our primary languages are Go and Ruby. Our platform runs on a Kubernetes cluster hosted on AWS, with PostgreSQL as our database of choice. We're also using Kafka for event-driven architecture and ELK for logging and auditing, among many other tools and services.   ➡️  What you can expect: our methodology, the Qonto Way We value upfront technical design, team reviews, and smart slicing, quality drives the velocity our customers need Our org assigns one team lead per project across stacks, giving your team a clear mission and full autonomy on how it operates Our lean toolbox, Kaizen, PDCA, lets you monitor and drive continuous improvement collaboratively with your peers and manager Each engineer gets access to Cursor or Copilot, and we want people who use AI to raise the bar, not just to go faster   ➡️  Your future team As a Backend Engineer, you will join one of Qonto's squad teams, each organised around a clear project mission, with one team lead and full ownership of a product scope. You'll be part of a 190-strong backend engineering community within a 500- Product Engineering department, working in squad teams of 5 to 10 people alongside Product, Design, and peers from Frontend and Mobile.   ➡️ About You Technical expertise: Strong backend engineering experience with exposure to large-scale web application architecture and real production constraints Stack fluency: You don't need to know our exact stack, but you transfer fast and are comfortable with similar technologies Quality-driven: You write tests, set up monitoring, handle incidents methodically, and do root cause analysis Ownership mindset: You track impact after release, flag regressions, and improve what's in your scope without being asked Team player: Comfortable working in squad teams with PMs, Designers, and other stacks; fluent in English

Vestiaire Collective

Vestiaire Collective is the leading global platform for desirable pre-loved fashion and a pioneer in transforming how people consume fashion.   Our mission is simple: make circular fashion the norm, not the exception. Through technology, expertise, and a highly engaged global community, we enable millions of people to buy and sell fashion in a more sustainable way.   Founded in Paris in 2009, Vestiaire Collective is now a globally scaled marketplace with offices in Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City, and logistics hubs across Europe, Asia, and the US. Today, we are a team of around 600 people from over 50 nationalities, united by a shared ambition: to drive meaningful change in the fashion industry.   Our values, Activism, Transparency, Dedication, Greatness, and Collective, shape how we build, collaborate, and grow every day. About the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).