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Help build ML systems that make financial crime harder to hide Financial crime is constantly changing. New patterns and behaviours emerge all the time, and the data we work with is complex. Our work helps make financial activity safer and more trustworthy for merchants, customers, and SumUp. As a Senior Data Science/ML Engineer in the Risk AI Engineering Squad, you will build the production systems that turn machine learning into reliable, explainable transaction-monitoring capabilities. You will work across the full model lifecycle: understanding financial-crime typologies, exploring data, engineering features, training and validating models, deploying them at scale, and monitoring their performance over time. This role is designed for someone who is strongest on the engineering side of machine learning and wants to keep growing their data-science depth. You do not need to be a traditional data scientist or ML Engineer. We’re looking for someone who enjoys working across both disciplines: building robust, production-ready software while staying close to the data, models, and decisions those systems support. You will join a cross-functional team within the Risk & Compliance tribe, working closely with AML and Fraud Operations, investigators, Product, and Engineering. Together, we build data products and ML solutions that help Risk teams work smarter, faster, and more effectively — while keeping our controls robust, auditable, and compliant across products and markets. We actively welcome applications from women and people from underrepresented backgrounds. Diverse perspectives make our team stronger and our systems more robust. If you're motivated by technical depth, real-world impact, and the challenge of making ML work reliably in a high-stakes environment, this role is built for you. What you’ll do Build ML systems that work in production Own and evolve end-to-end batch training pipelines for transaction-monitoring models. Build reliable software around the model lifecycle, including testing, CI/CD, versioning, deployment, monitoring, and rollback. Improve the maintainability, observability, and scalability of our model pipelines. Partner with platform and software engineers to make model delivery repeatable and safe. Turn data and domain knowledge into better detection Build, maintain, and improve ML models for transaction monitoring, balancing detection quality, operational efficiency, explainability, and regulatory expectations. Engineer features that reflect AML and Fraud typologies and suspicious behaviours. Work with Risk investigators to translate domain knowledge into useful signals, alerting logic, and calibrated thresholds. Analyse the drivers of the AML Risk Score and recommend improvements to its features, logic, and thresholds. Keep models trustworthy over time Define and track meaningful model and operational metrics, including detection performance, alert volumes, and investigator outcomes. Monitor drift and model health, run back-testing, and investigate changes in performance. Run sensitivity tests on synthetic datasets and assess how models behave across relevant scenarios and populations. Produce model cards, technical documentation, and other ML governance artefacts that support auditability and regulatory review. Contribute to system-design documentation and adapt solutions to regional compliance requirements. Work across disciplines Partner with AML and Fraud Operations, Product, and Engineering to turn ambiguous problems into clear, scalable technical plans. Explain trade-offs clearly to both technical and non-technical stakeholders. Help the team improve its engineering practices, modelling approach, and understanding of financial-crime risk. Share what you learn and support a culture of thoughtful experimentation, constructive challenge, and continuous improvement. You’ll be great for this role if you have… Must have Strong production Python engineering experience. You write code that ships and are comfortable with automated testing, CI/CD, code review, versioning, observability, and operating services or pipelines in production. Experience deploying and operating ML models in production. You understand the practical realities beyond experimentation: reproducible training, model versioning, deployment, monitoring, incident response, and rollback. Hands-on experience with end-to-end ML pipelines. You have taken models from data preparation and training through validation and production use, and you understand how to choose appropriate KPIs and evaluation metrics. Solid data-engineering fundamentals. You have worked with complex, multi-source data ecosystems and care about data quality, lineage, reproducibility, and failure modes. A willingness to deepen your data-science expertise. You are interested in modelling, feature engineering, evaluation, and experimentation — even if your background is primarily in ML engineering or software engineering. Clear, confident communication. You can align stakeholders, set expectations, surface risks, and turn ambiguous compliance or operational requirements into a concrete technical plan. Nice to have Experience with PySpark or other distributed data-processing technologies. Experience in AML, fraud detection, transaction monitoring, or another financial-crime domain. Experience with unsupervised learning, such as anomaly detection or clustering. Familiarity with feature stores, model registries, and alerting-threshold calibration. Experience producing ML governance artefacts, such as model cards, validation reports, or audit documentation. Experience with AI systems and tooling. This role could be a strong fit if… You are an ML engineer who wants to become more involved in modelling and data science. You are a software or data engineer who has already shipped ML systems and wants to own more of the model lifecycle. You are a data scientist who genuinely enjoys production engineering, automation, testing, and operating models — not only training them in notebooks. You like working where technical decisions have a real-world impact and where reliability, explainability, and governance matter as much as model pe Why you should join SumUp 🌎 Opportunity to work with SumUppers globally on large-scale fintech products used by millions of businesses worldwide, from our Berlin office. This involves an office-first setup 🌈 Commitment to Diversity and Inclusion: be part of a workplace that values and promotes diversity, fostering an inclusive environment where everyone's perspectives are respected and embraced 🚀 Enrolment onto our Virtual Stock Option programme: you will own a stake in SumUp's future success 📚 A dedicated annual L&D budget of €2,000 for your individual development, which can be used to attend conferences and/or advance your career through further education 💶 A corporate pension scheme where we match up to 20% of your contributions 🏖️ Generous time off: enjoy 28 days of paid leave plus public holidays and special leave days 💪 Numerous other benefits such as Urban Sports Club subsidy, Kita placement assistance, and subsidised office lunches 🌴 Break4me: 1-month sabbatical after 3 years of service 🔗 Referral Bonus: earn additional rewards by referring talented individuals to join the SumUp team About SumUp Be empowered to do more that matters. At SumUp, we're on a mission to empower small businesses across the globe by providing simple and affordable tools that allow them to thrive. Today, over 4 million businesses in 37 markets rely on SumUp as their financial partner to manage payments, finance and customer relationships. Our commitment to small businesses is reflected in our diverse team of over 3,000 SumUppers from over 90 nationalities, united by global collaboration and an innovative mindset. Our core values lay the foundation for who we are and what we stand for, shaping our work culture and driving our success. We foster inclusivity and a continuous learning culture, providing a safe space for personal and professional growth. Our differences make us unique and strong as we strive to create an environment where everyone belongs and feels supported, no matter how they identify. SumUp is proud to be an Equal Employment Opportunity employer, actively seeking and embracing diversity in our workforce. We don't make hiring or employment decisions based on race, colour, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age or any other basis protected by applicable laws or prohibited by company policy. Our commitment extends beyond recruitment to creating a safe and respectful workplace where harassment of any form is strictly prohibited. Discover more about our culture and opportunities on our careers website , and follow our journey on LinkedIn , Instagram , and TikTok . Job Application Tip We recognise that candidates feel they need to meet 100% of the job criteria in order to apply for a job. Please note that this is only a guide. If you don’t tick every box, it’s ok too because it means you have room to learn and develop your career at SumUp.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.