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Before you apply to a job, select your language preference from the options available at the top right of this page. Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrowpeople with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level. Job Description: About Machine Learning Engineering at UPS Technology: Were the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done our innovative culture demands yes and how! We are UPS. We are the United Problem Solvers. Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise. About this Role: We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. Youll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPSs strategic goals. You will contribute across the full ML lifecyclefrom data exploration and feature engineering to model training, evaluation, deployment, and monitoring. Youll also help shape our MLOps practices and mentor junior engineers. Job SummaryThe Marketing ML Engineer / ML Ops Engineer is responsible for operationalizing machine learning models within the marketing technology ecosystem. This role ensures production-grade deployment, low-latency inference, reliable data refresh cycles, and fully automated model pipelines. The position bridges Data Science and Engineering by transforming experimental models into scalable, monitored, and business-ready solutions within the Global Customer Platform. What They Will Build & OperationalizeThe ML Engineer will deploy and manage: Production-ready marketing ML models including: Propensity to Buy (PTB) Churn Prediction Customer Lifetime Value (CLV) Automated training and inference pipelines Real-time or batch scoring workflows Feature store infrastructure for reusable, governed features Model monitoring and drift detection systems CI/CD-enabled ML deployment pipelines Their work directly supports personalization, targeting, retention strategies, and revenue optimization initiatives. Key Responsibilities1. Model Deployment & ProductionizationDeploy ML models into the Global Customer Platform. Ensure low-latency inference for real-time decisioning where required. Enable scalable batch scoring pipelines. Eliminate manual scoring processes through automation. 2. Pipeline AutomationBuild automated training and retraining workflows. Develop CI/CD pipelines for ML lifecycle management. Ensure consistent data refresh cycles aligned with SLA requirements. Reduce operational handoffs between Data Science and Engineering teams. 3. Model Monitoring & GovernanceMonitor model performance in production environments. Detect and mitigate model drift (data drift & concept drift). Track prediction accuracy, stability, and bias metrics. Maintain versioning and reproducibility standards. 4. Feature Engineering & Data InfrastructureDesign and maintain feature stores. Ensure feature consistency between training and inference environments. Optimize data pipelines for reliability and scalability. Collaborate with data engineering teams on data schema and quality controls. Required Skills & Experience510+ years in data engineering, ML engineering, or MLOps roles Strong experience deploying ML models into production environments Proficiency in Python and ML frameworks (e.g., Scikit-learn, XGBoost, TensorFlow, PyTorch) Experience with orchestration tools (Airflow, Kubeflow, or similar) Familiarity with containerization and deployment (Docker, Kubernetes) Experience with cloud platforms (Azure, AWS, or GCP) Strong understanding of feature stores and model lifecycle management Knowledge of monitoring tools for drift detection and model performance Preferred QualificationsExperience working in marketing analytics or customer data platforms Familiarity with CDP integrations and real-time personalization systems Understanding of customer segmentation and campaign activation workflows Experience implementing ML governance and compliance standards Employee Type: Permanent UPS is committed to providing a workplace free of discrimination, harassment, and .
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.