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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Machine Learning Engineer - **Company:** Uber - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $202,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Big Data, Apache Hive, Python (Programming Language), Machine Learning, Tensorflow, Standard Sql, Reinforcement Learning, Pytorch, Apache Spark, Backend, Scikit Learn, Information Technology, Power Analysis (Cryptography), Xgboost, Presto - **Published:** September 18, 2026 - **Apply:** https://dejobs.org/x/x/1AA9DFCDCB77493CBF64D2F6582E9FE2/job/ ## About the Role * Bachelor's degree in Computer Science, Statistics, Economics, Operations Research, or a related quantitative field, or equivalent practical experience. * 5+ years of experience building and shipping ML models that drive product or business decisions in production. * Strong proficiency in Python and modern ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM or equivalent). * Strong SQL and hands-on experience with large-scale data processing (Spark, Hive, Presto, or comparable). * Demonstrated experience with experimental design and analysis - A/B testing, power analysis, variance reduction, and interpreting noisy results responsibly. * Experience building a model across all lifecycle stages: from notebook to production pipelines, serving, monitoring, retraining, and deployment. * Ability to explain a modeling decision and its business consequences clearly to technical and non-technical audiences alike Preferred Qualifications * Experience training deep feed-forward models (MLP) for uplift estimation. * Experience with constrained optimization applied to resource allocation (LP/MIP, Lagrangian duality, dual-price or bidding-style budget pacing). * Experience with incentive, promotion, pricing, or discount targeting at consumer scale. * Experience with contextual bandits or reinforcement learning for sequential decisioning. * Familiarity with subscription businesses: trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement. * Experience leading technical direction across an ambiguous, cross-functional scope. ## Description * Own the end-to-end lifecycle of targeting and personalization models - problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring. * Build heterogeneous treatment effect models that predict the incremental impact of interventions on users. * Design budget-constrained allocation systems that turn per-user uplift predictions into offer decisions under real constraints (incentive budget, variable contribution targets, cannibalization of full-price conversion, per-surface frequency caps). * Build personalized ranking and sequencing models for membership messaging across Eats and Mobility apps - balancing conversion against user experience and contention with non-membership content. * Partner with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, and make sure they behave in production the way they did offline. * Work across Product, Engineering, Data Science, Finance, and Marketing to translate fuzzy business goals into concrete ML problem statements. ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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