About the job
Uber Direct powers fast, reliable delivery for enterprise retailers and local businesses by leveraging Uber’s world-class logistics network. As a Senior Machine Learning Engineer on the Uber Direct team, you will define and build intelligent systems that improve operational efficiency, customer experience, and predictive capabilities in real-time logistics at global scale. You’ll partner closely with Product, Data Science, and Engineering teams to design, deploy, and continually enhance machine learning-driven solutions that power core decision-making across the delivery lifecycle. Your work will directly influence key marketplace and logistics metrics across millions of global deliveries.
Responsibilities
Develop High-Impact ML Solutions: Design, build, and productionize machine learning models that solve critical logistics problems such as ETA prediction, demand forecasting, dispatch optimization, anomaly detection, and delivery quality improvements.
Own the End-to-End ML Lifecycle: Lead projects from problem definition and data exploration through feature engineering, model development, evaluation, deployment, monitoring, and iteration.
Build Scalable ML Systems: Develop robust data pipelines, feature stores, training workflows, and model serving infrastructure that support both real-time and batch inference at scale.
Drive Business Impact: Define success metrics, run experiments, and rigorously evaluate model performance to ensure measurable improvements to KPIs such as Completion Rate, On-Time Rate, and Defect Rate.
Collaborate Cross-Functionally: Work closely with Product Managers, Data Scientists, Operations, and Backend Engineers to translate business problems into scalable ML solutions.
Technical Leadership & Mentorship: Provide technical direction, establish best practices in ML and MLOps, and mentor engineers across the team.
Qualifications
Minimum
Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field, or equivalent practical experience.
5+ years of experience building and shipping production-grade machine learning systems.
Strong proficiency in Python, plus experience with at least one additional programming language (e.g., Go, Java, C++, Scala).
Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or Scikit-Learn.
Demonstrated experience deploying, monitoring, and maintaining ML models in production environments.
Solid understanding of statistics, feature engineering, model evaluation methodologies, and experimental design.
Strong software engineering fundamentals, including data structures, algorithms, and system design.
Preferred
Master’s or PhD in Machine Learning, Computer Science, Statistics, or related field.
Experience building large-scale ML systems in a high-throughput, low-latency production environment.
Background in logistics, marketplace systems, forecasting, optimization, recommendation systems, or time-series modeling.
Experience with distributed data processing frameworks (e.g., Spark, Hive) and streaming systems (e.g., Kafka).
Familiarity with MLOps tooling such as Airflow, Kubeflow, MLflow, feature stores, and CI/CD pipelines for ML workflows.
Experience with A/B testing, experimentation frameworks, and causal inference.
Proven ability to optimize ML systems for scalability, reliability, observability, and latency.
Experience mentoring engineers and contributing to technical strategy.