(RD) Staff Machine Learning Engineer at Workatbackbase

The Job in short Over 75 million people interact with banking products built on Backbase. Every model you ship here touches real accounts, real decisions, and real financial lives - at scale. That's the standard this role is built around. Backbase leads in AI-native banking technology, helping the world's biggest banks move from fragmented systems to unified frontlines where humans and AI agents work together. We power 100+ of the largest banks globally, and our AI capabilities sit at the core of that shift. As a Principal Machine Learning Engineer, you will define how intelligent systems operate in this environment: not just predicting outcomes, but making safe, auditable, and real-time decisions within a controlled execution model. Reasons to build with us Real-world impact at scale: Your models run in production for 150+ million end users across the globe's leading banks. AI at the core: We're building the AI-native Banking OS from the ground up - ML engineering here shapes the product direction, not just the feature roadmap. High-ownership culture: We ship early, iterate fast, and trust engineers to make decisive calls. You'll move at speed without layers of approval slowing you down. "We don't debate AI in the abstract - we build it and put it in front of real banks. The engineers who thrive here are the ones who start with the problem, own the outcome, and raise the bar every sprint. That's the culture we've built, and it shows in what we ship. - VP of Engineering, AI & Data Meet the job You'll be a key architect of Backbase's AI capabilities. That means moving fast, owning your work end-to-end, and building ML systems that solve real problems for banking partners - not theoretical models that gather dust. That means: ● Designing, developing, and deploying production-grade ML models that personalize the banking experience and automate complex financial workflows ● Partnering with product and data teams to integrate AI capabilities directly into the Banking Platform ● Taking full ownership of the end-to-end ML lifecycle - from data discovery and feature engineering through to model monitoring and retraining ● Turning complex data architectures into clean, maintainable ML pipelines and APIs ● Running rigorous code reviews and mentoring mid-level engineers to keep raising the technical bar ● Identifying opportunities to apply AI where it removes friction and adds measurable value for end users How about you You're a seasoned engineer who thrives in a straight-talk culture. You don't settle for the industry standard - yesterday's best work is today's baseline, and you're always looking for what's next. Deep technical chops and a customer-first mindset aren't in tension for you; they're the same thing. Your track record also shows: ● 5+ years designing and deploying large-scale ML systems in production environments ● Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, etc.) ● Experience with LLMs, RAG, or agent-based systems in production Solid MLOps experience: CI/CD for ML model versioning monitoring and observability ● Experience with data and streaming systems (e.g., Spark, Kafka) ● The ability to communicate complex technical concepts clearly to non-technical stakeholders, without oversimplifying or over-complicating ● A proven ability to make decisive technical choices, meet deadlines, and keep momentum in a fast-moving environment Why Backbase Backbase is where ambitious engineers come to do the most consequential work of their careers. We're growing fast, we build in the open, and we hold ourselves accountable to outcomes that show up in the real world - not just on slides. We also offer: ● Competitive salary and performance-based bonus ● Flexible working arrangements and a hybrid setup ● Access to top-tier tools, cloud infrastructure, and ML platforms ● A learning budget to keep your skills sharp and your career moving Collaborative teams across Amsterdam, Atlanta, Bangalore, and beyond ● A culture where straight talk is valued and good ideas win regardless of where they come from ● The chance to build AI systems that run at the scale of global banking - and see the results