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Who we are About the team Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers. What you'll do We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions Mentor engineers and contribute to a strong ML engineering culture within the team Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of industry experience building and shipping ML systems in production Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark Hands-on experience in designing, training, and evaluating machine learning models Hands-on experience in productionizing and deploying models at scale Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets Strong collaboration skills and the ability to work across teams and contribute to peers' success Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset Preferred qualifications MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science) Experience in fintech, open banking, or financial data domains Experience with NLP, LLMs, or text classification at scale Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems Experience with deep learning architectures, including transformers
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.